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Majidifar S, Zabihian A, Hooshmand M. Combination therapy synergism prediction for virus treatment using machine learning models. PLoS One 2024; 19:e0309733. [PMID: 39231124 PMCID: PMC11373828 DOI: 10.1371/journal.pone.0309733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 08/16/2024] [Indexed: 09/06/2024] Open
Abstract
Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on computational prediction for new combination therapies, there is limited to no research on combination therapies in the treatment of viral diseases. This paper proposes AI-based models for predicting novel antiviral combinations to treat virus diseases synergistically. To do this, we assembled a comprehensive dataset comprising information on viral strains, drug compounds, and their known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes using a random forest model, an SVM model, and a deep model to train viral combination therapy. The machine learning models showed the highest performance, and the predicted values were validated by a t-test, indicating the effectiveness of the proposed methods. One of the predicted combinations of acyclovir and ribavirin has been experimentally confirmed to have a synergistic antiviral effect against herpes simplex type-1 virus, as described in the literature.
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Affiliation(s)
- Shayan Majidifar
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Arash Zabihian
- Department of QA, Kimia Zist Parsian Pharmaceutical Company, Zanjan, Iran
| | - Mohsen Hooshmand
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
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2
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Yang T, Li H, Kang Y, Li Z. MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect. Biomolecules 2024; 14:1039. [PMID: 39199425 PMCID: PMC11352627 DOI: 10.3390/biom14081039] [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: 06/30/2024] [Revised: 08/10/2024] [Accepted: 08/16/2024] [Indexed: 09/01/2024] Open
Abstract
Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.
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Affiliation(s)
- Tao Yang
- School of Information Engineering, Huzhou University, Huzhou 313000, China;
- College of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China;
| | - Haohao Li
- College of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China;
| | - Yanlei Kang
- School of Information Engineering, Huzhou University, Huzhou 313000, China;
| | - Zhong Li
- School of Information Engineering, Huzhou University, Huzhou 313000, China;
- College of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China;
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3
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Zhang H, Chen Y, Payne P, Li F. Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails. NPJ Syst Biol Appl 2024; 10:92. [PMID: 39169016 PMCID: PMC11339460 DOI: 10.1038/s41540-024-00421-w] [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: 02/13/2024] [Accepted: 08/05/2024] [Indexed: 08/23/2024] Open
Abstract
Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow.
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Affiliation(s)
- Heming Zhang
- Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine, St. Louis, MO, USA
| | - Yixin Chen
- Computer Science, Washington University School of Medicine, St. Louis, MO, USA
| | - Philip Payne
- Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine, St. Louis, MO, USA
| | - Fuhai Li
- Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine, St. Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
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4
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Vis DJ, Jaaks P, Aben N, Coker EA, Barthorpe S, Beck A, Hall C, Hall J, Lightfoot H, Lleshi E, Mironenko T, Richardson L, Tolley C, Garnett MJ, Wessels LFA. A pan-cancer screen identifies drug combination benefit in cancer cell lines at the individual and population level. Cell Rep Med 2024; 5:101687. [PMID: 39168097 PMCID: PMC11384948 DOI: 10.1016/j.xcrm.2024.101687] [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: 01/22/2024] [Revised: 05/10/2024] [Accepted: 07/23/2024] [Indexed: 08/23/2024]
Abstract
Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce "efficacious combination benefit" (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers in vitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.
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Affiliation(s)
- Daniel J Vis
- Department of EEMCS, Delft University of Technology, the Netherlands
| | | | - Nanne Aben
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | | | | | | | | | - James Hall
- Wellcome Sanger Institute, Cambridge, UK
| | | | | | | | | | | | | | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands.
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5
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Dong F, Yan J, Zhang X, Zhang Y, Liu D, Pan X, Xue L, Liu Y. Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery. Heliyon 2024; 10:e35742. [PMID: 39170321 PMCID: PMC11336844 DOI: 10.1016/j.heliyon.2024.e35742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 07/27/2024] [Accepted: 08/02/2024] [Indexed: 08/23/2024] Open
Abstract
Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.
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Affiliation(s)
- Fanqiao Dong
- School of Stomatology, China Medical University, Shenyang, China
| | - Jingjing Yan
- Hospital of Stomatology, China Medical University, Shenyang, China
| | - Xiyue Zhang
- School of Stomatology, China Medical University, Shenyang, China
| | - Yikun Zhang
- School of Stomatology, China Medical University, Shenyang, China
| | - Di Liu
- School of Stomatology, China Medical University, Shenyang, China
| | - Xiyun Pan
- School of Stomatology, China Medical University, Shenyang, China
| | - Lei Xue
- School of Stomatology, China Medical University, Shenyang, China
- Hospital of Stomatology, China Medical University, Shenyang, China
| | - Yu Liu
- First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
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Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K, Rashidi P. Transformers and large language models in healthcare: A review. Artif Intell Med 2024; 154:102900. [PMID: 38878555 DOI: 10.1016/j.artmed.2024.102900] [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: 09/01/2023] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 08/09/2024]
Abstract
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of healthcare data, including clinical NLP, medical imaging, structured Electronic Health Records (EHR), social media, bio-physiological signals, biomolecular sequences. Furthermore, which have also include the articles that used the transformer architecture for generating surgical instructions and predicting adverse outcomes after surgeries under the umbrella of critical care. Under diverse settings, these models have been used for clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. Finally, we also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.
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Affiliation(s)
- Subhash Nerella
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | | | - Jiaqing Zhang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, United States
| | - Miguel Contreras
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Scott Siegel
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Aysegul Bumin
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, United States
| | - Brandon Silva
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, United States
| | - Jessica Sena
- Department Of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Benjamin Shickel
- Department of Medicine, University of Florida, Gainesville, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, United States
| | - Kia Khezeli
- Department of Biomedical Engineering, University of Florida, Gainesville, United States
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, United States.
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7
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Tang YC, Li R, Tang J, Zheng WJ, Jiang X. SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations. BMC Bioinformatics 2024; 25:250. [PMID: 39080535 PMCID: PMC11290087 DOI: 10.1186/s12859-024-05873-9] [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: 04/23/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and higher-order relationships. These limitations constrain the applicability of current methods. RESULTS We introduce SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis. CONCLUSIONS SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Furthermore, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients and can be applied to prioritize personalized effective treatment based on safe dose combinations.
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Affiliation(s)
- Yi-Ching Tang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, USA
| | - Rongbin Li
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, 00290, Helsinki, Finland
| | - W Jim Zheng
- Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoqian Jiang
- Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, USA.
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8
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AlJarf R, Rodrigues CHM, Myung Y, Pires DEV, Ascher DB. piscesCSM: prediction of anticancer synergistic drug combinations. J Cheminform 2024; 16:81. [PMID: 39030592 PMCID: PMC11264925 DOI: 10.1186/s13321-024-00859-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 05/12/2024] [Indexed: 07/21/2024] Open
Abstract
While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. SCIENTIFIC CONTRIBUTION: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.
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Affiliation(s)
- Raghad AlJarf
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia.
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia.
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9
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Oikonomou A, Watrin T, Valsecchi L, Scharov K, Savino AM, Schliehe-Diecks J, Bardini M, Fazio G, Bresolin S, Biondi A, Borkhardt A, Bhatia S, Cazzaniga G, Palmi C. Synergistic drug interactions of the histone deacetylase inhibitor givinostat (ITF2357) in CRLF2-rearranged pediatric B-cell precursor acute lymphoblastic leukemia identified by high-throughput drug screening. Heliyon 2024; 10:e34033. [PMID: 39071567 PMCID: PMC11277435 DOI: 10.1016/j.heliyon.2024.e34033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 07/02/2024] [Accepted: 07/02/2024] [Indexed: 07/30/2024] Open
Abstract
Combining multiple drugs broadens the window of therapeutic opportunities and is crucial for diseases that are currently lacking fully curative treatments. A powerful emerging tool for selecting effective drugs and combinations is the high-throughput drug screening (HTP). The histone deacetylase inhibitor (HDACi) givinostat (ITF2357) has been shown to act effectively against CRLF2-rearranged pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL), a subtype characterized by poor outcome and enriched in children with Down Syndrome, very fragile patients with a high susceptibility to treatment-related toxicity. The aim of this study is to investigate possible synergies with givinostat for these difficult-to-treat patients by performing HTP screening with a library of 174 drugs, either approved or in preclinical studies. By applying this approach to the CRLF2-r MHH-CALL-4 cell line, we identified 19 compounds with higher sensitivity in combination with givinostat compared to the single treatments. Next, the synergy between givinostat and the promising candidates was further validated in CRLF2r cell lines with a broad matrix of concentrations. The combinations with trametinib (MEKi) or venetoclax (BCL2i) were found to be the most effective and with the greatest synergy across three metrics (ZIP, HAS, Bliss). Their efficacy was confirmed in primary blasts treated ex vivo at concentration ranges with a safe profile on healthy cells. Finally, we described givinostat-induced modifications in gene expression of MAPK and BCL-2 family members, supporting the observed synergistic interactions. Overall, our study represents a model of drug repurposing strategy using HTP screening for identifying synergistic, efficient, and safe drug combinations.
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Affiliation(s)
| | - Titus Watrin
- Department of Paediatric Oncology, Haematology and Clinical Immunology, Heinrich-Heine University Dusseldorf, Medical Faculty, Düsseldorf, Germany
| | - Luigia Valsecchi
- Tettamanti Center, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Katerina Scharov
- Department of Paediatric Oncology, Haematology and Clinical Immunology, Heinrich-Heine University Dusseldorf, Medical Faculty, Düsseldorf, Germany
| | - Angela Maria Savino
- Tettamanti Center, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Julian Schliehe-Diecks
- Department of Paediatric Oncology, Haematology and Clinical Immunology, Heinrich-Heine University Dusseldorf, Medical Faculty, Düsseldorf, Germany
| | - Michela Bardini
- Tettamanti Center, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Grazia Fazio
- Tettamanti Center, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Silvia Bresolin
- Pediatric Hematology, Oncology and Stem Cell Transplant Division, Women and Child Health Department, Padua University and Hospital, Padua, Italy
- Onco-Hematology, Stem Cell Transplant and Gene Therapy, Istituto di Ricerca Pediatrica Foundation - Città della Speranza, Padua, Italy
| | - Andrea Biondi
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Pediatrics, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Arndt Borkhardt
- Department of Paediatric Oncology, Haematology and Clinical Immunology, Heinrich-Heine University Dusseldorf, Medical Faculty, Düsseldorf, Germany
| | - Sanil Bhatia
- Department of Paediatric Oncology, Haematology and Clinical Immunology, Heinrich-Heine University Dusseldorf, Medical Faculty, Düsseldorf, Germany
| | - Giovanni Cazzaniga
- Tettamanti Center, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Chiara Palmi
- Tettamanti Center, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
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10
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Chen S, Gao N, Li C, Zhai F, Jiang X, Zhang P, Guan J, Li K, Xiang R, Ling G. DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases. J Chem Inf Model 2024; 64:5317-5327. [PMID: 38900583 DOI: 10.1021/acs.jcim.4c00296] [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: 06/22/2024]
Abstract
Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.
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Affiliation(s)
- Siqi Chen
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Nan Gao
- Wuya College of Innovation, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Chunzhi Li
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Fei Zhai
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Xiwei Jiang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Peng Zhang
- Wuya College of Innovation, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
| | - Jibin Guan
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Kefeng Li
- Center for Artificial Intelligence-Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SR 999708, China
| | - Rongwu Xiang
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
- Liaoning Medical Big Data and Artificial Intelligence Engineering Technology Research Center, Shenyang 110016, China
| | - Guixia Ling
- College of Medical Devices, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
- Wuya College of Innovation, Shenyang Pharmaceutical University, 103 Wenhua Road, Shenyang 110016, China
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11
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Dong Y, Bai Y, Liu H, Yang Z, Chang Y, Li J, Han Q, Feng X, Fan X, Ren X. DKPE-GraphSYN: a drug synergy prediction model based on joint dual kernel density estimation and positional encoding for graph representation. Front Genet 2024; 15:1401544. [PMID: 38948360 PMCID: PMC11211516 DOI: 10.3389/fgene.2024.1401544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/24/2024] [Indexed: 07/02/2024] Open
Abstract
Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-to-end learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model's superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer.
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Affiliation(s)
- Yunyun Dong
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yujie Bai
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Haitao Liu
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Ziting Yang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yunqing Chang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Jianguang Li
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Qixuan Han
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xiufang Feng
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xiaole Fan
- Information Management Department, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi, China
| | - Xiaoqiang Ren
- Information Management Department, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi, China
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12
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Abbasi F, Rousu J. New methods for drug synergy prediction: A mini-review. Curr Opin Struct Biol 2024; 86:102827. [PMID: 38705070 DOI: 10.1016/j.sbi.2024.102827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 05/07/2024]
Abstract
In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these articles under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the articles deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.
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Affiliation(s)
- Fatemeh Abbasi
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Juho Rousu
- Department of Computer Science, Aalto University, Espoo, Finland.
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13
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Huangfu X, Zhang C, Li H, Li S, Li Y. SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations. Comput Biol Chem 2024; 110:108054. [PMID: 38522389 DOI: 10.1016/j.compbiolchem.2024.108054] [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: 10/25/2023] [Revised: 02/22/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN. This framework only requires a small amount of auxiliary data, like genomics information of cell lines and the molecular fingerprints or targets of drugs. Based on the assumption that similar cell lines and similar drug combinations have similar synergistic effects, CLSN and DCSN predict synergy scores through capturing individual synergy contributions of nearest cell line and drug combination neighbors, respectively. High correlations between predicted and measured synergy scores on two leading cancer cell line pharmacogenomic screening datasets (the O'Neil dataset and the NCI-ALMANAC dataset) demonstrate the effectiveness and robustness of SNSynergy. Many of the identified drug combinations are consistent with previous studies, or have been explored in clinical settings against the specific cancer type, showing that SNSynergy has the potential to supply cost-saving and effective high-throughput screening for prioritizing the most applicable cell lines and the most promising drug combinations.
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Affiliation(s)
- Xiaosheng Huangfu
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Chengwei Zhang
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Hualong Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China; School of mathematics and statistics, Guangdong University of Technology, Guangzhou 510520, China
| | - Sile Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Yushuang Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China.
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14
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Su R, Han J, Sun C, Zhang D, Geng J, Wang P, Zeng X. Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes. Comput Biol Med 2024; 175:108441. [PMID: 38663353 DOI: 10.1016/j.compbiomed.2024.108441] [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: 01/11/2024] [Revised: 03/17/2024] [Accepted: 04/07/2024] [Indexed: 05/15/2024]
Abstract
At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug resistance and reduce drug toxicity in cancer treatment. High-throughput screening through deep learning can effectively improve the efficiency of discovering synergistic drugs. Nowadays, most of the existing deep learning algorithms for anti-cancer drug synergy prediction use deep neural networks and can only implicitly perform feature interaction. This study proposes a deep learning algorithm, named MolCross, which combines implicit feature interaction with explicit features to improve the accuracy of prediction of the anti-cancer drug synergy score. MolCross uses a deep autoencoder to extract features from high-dimensional input, uses the drug-specific subnetworks and cross-network to perform implicit feature interaction and explicit feature interaction respectively, and finally uses a synergy prediction network to combine the two feature interaction methods to obtain the final prediction results. We adopted a five-fold cross validation and compared MolCross with other four anti-cancer drug synergy prediction models. The results show that MolCross has better prediction performance than other models. MolCross also has good performance in terms of cross-cell line and cross-tissue type. Existing studies have demonstrated that cancer molecular subtypes have different sensitivities to targeted therapy. In this study, the features of cancer molecular subtype were introduced in the model using an embedding layer in MolCross to explore the effect of cancer molecular subtype on anti-cancer drug synergy. We also found that the cancer molecular subtype is one of the main factors affecting the synergy between drugs.
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Affiliation(s)
- Ran Su
- School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
| | - Jingyi Han
- School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
| | | | - Degan Zhang
- Tianjin Key Lab of Intelligent Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China.
| | - Jie Geng
- TianJin Chest Hospital, Tianjin University, No. 261, Taierzhuang South Road, Jinnan District, Tianjin, China.
| | - Ping Wang
- Tianjin Modern Innovative TCM Technology Co. Ltd., Tianjin, 300392, China.
| | - Xiaoyan Zeng
- The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, 646000, China.
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15
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John AJ, Ghose ET, Gao H, Luck M, Jeong D, Kalari KR, Wang L. ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery. Front Oncol 2024; 14:1343091. [PMID: 38884087 PMCID: PMC11176476 DOI: 10.3389/fonc.2024.1343091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 04/18/2024] [Indexed: 06/18/2024] Open
Abstract
Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.
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Affiliation(s)
- August J John
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Emily T Ghose
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Huanyao Gao
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Meagan Luck
- Department of Biological Sciences, University of Notre Dame, South Bend, IN, United States
| | - Dabin Jeong
- Biochemistry Department, Lawrence University, Appleton, WI, United States
| | - Krishna R Kalari
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
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16
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Pang Y, Chen Y, Lin M, Zhang Y, Zhang J, Wang L. MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations. J Chem Inf Model 2024; 64:3689-3705. [PMID: 38676916 DOI: 10.1021/acs.jcim.4c00165] [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: 04/29/2024]
Abstract
Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.
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Affiliation(s)
- Yu Pang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yihao Chen
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Mujie Lin
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yanhong Zhang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Jiquan Zhang
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, College of Pharmacy, Guizhou Medical University, Guiyang 550025, P. R. China
| | - Ling Wang
- Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
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17
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Munson BP, Chen M, Bogosian A, Kreisberg JF, Licon K, Abagyan R, Kuenzi BM, Ideker T. De novo generation of multi-target compounds using deep generative chemistry. Nat Commun 2024; 15:3636. [PMID: 38710699 DOI: 10.1038/s41467-024-47120-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 03/18/2024] [Indexed: 05/08/2024] Open
Abstract
Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.
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Affiliation(s)
- Brenton P Munson
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Michael Chen
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Audrey Bogosian
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jason F Kreisberg
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Katherine Licon
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Brent M Kuenzi
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Trey Ideker
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
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18
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Han J, Kang MJ, Lee S. DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile. Comput Biol Med 2024; 174:108436. [PMID: 38643597 DOI: 10.1016/j.compbiomed.2024.108436] [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: 01/25/2024] [Revised: 04/01/2024] [Accepted: 04/07/2024] [Indexed: 04/23/2024]
Abstract
Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (DRug Synergy PRediction by INtegrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods.
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Affiliation(s)
- Jiyeon Han
- Department of Bio-Information Science, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Min Ji Kang
- Department of Life Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Sanghyuk Lee
- Department of Bio-Information Science, Ewha Womans University, Seoul, 03760, Republic of Korea; Department of Life Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea.
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19
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Bashi AC, Coker EA, Bulusu KC, Jaaks P, Crafter C, Lightfoot H, Milo M, McCarten K, Jenkins DF, van der Meer D, Lynch JT, Barthorpe S, Andersen CL, Barry ST, Beck A, Cidado J, Gordon JA, Hall C, Hall J, Mali I, Mironenko T, Mongeon K, Morris J, Richardson L, Smith PD, Tavana O, Tolley C, Thomas F, Willis BS, Yang W, O'Connor MJ, McDermott U, Critchlow SE, Drew L, Fawell SE, Mettetal JT, Garnett MJ. Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations. Cancer Discov 2024; 14:846-865. [PMID: 38456804 PMCID: PMC11061612 DOI: 10.1158/2159-8290.cd-23-0388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 11/01/2023] [Accepted: 02/02/2024] [Indexed: 03/09/2024]
Abstract
Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific "emergent" biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets. SIGNIFICANCE We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.
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Affiliation(s)
| | | | | | | | | | | | - Marta Milo
- Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | | | | | | | | | - Syd Barthorpe
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | | | | | | | - Caitlin Hall
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | - James Hall
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | - Iman Mali
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | - James Morris
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | - Paul D. Smith
- Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | - Omid Tavana
- Oncology R&D, AstraZeneca, Waltham, Massachusetts
| | | | | | | | - Wanjuan Yang
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | | | - Lisa Drew
- Oncology R&D, AstraZeneca, Waltham, Massachusetts
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20
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Liu Y, Zhang P, Che C, Wei Z. SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction. J Chem Inf Model 2024. [PMID: 38687366 DOI: 10.1021/acs.jcim.4c00177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Drug combination therapies are well-established strategies for the treatment of cancer with low toxicity and fewer adverse effects. Computational drug synergy prediction approaches can accelerate the discovery of novel combination therapies, but the existing methods do not explicitly consider the key role of important substructures in producing synergistic effects. To this end, we propose a significant substructure-aware anticancer drug synergy prediction method, named SDDSynergy, to adaptively identify critical functional groups in drug synergy. SDDSynergy splits the task of predicting drug synergy into predicting the effect of individual substructures on cancer cell lines and highlights the impact of important substructures through a novel drug-cell line attention mechanism. And a substructure pair attention mechanism is incorporated to capture the information on internal substructure pairs interaction in drug combinations, which aids in predicting synergy. The substructures of different sizes and shapes are directly obtained from the molecular graph of the drugs by multilayer substructure information passing networks. Extensive experiments on three real-world data sets demonstrate that SDDSynergy outperforms other state-of-the-art methods. We also verify that many of the novel drug combinations predicted by SDDSynergy are supported by previous studies or clinical trials through an in-depth literature survey.
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Affiliation(s)
- Yunjiong Liu
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China
- School of Software Engineering, Dalian University, Dalian 116622, China
| | - Peiliang Zhang
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
| | - Chao Che
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China
- School of Software Engineering, Dalian University, Dalian 116622, China
| | - Ziqi Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100864, China
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21
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Zhao X, Xu J, Shui Y, Xu M, Hu J, Liu X, Che K, Wang J, Liu Y. PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction. J Cheminform 2024; 16:41. [PMID: 38622663 PMCID: PMC11017561 DOI: 10.1186/s13321-024-00839-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
MOTIVATION Drug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines. RESULTS In this paper, we proposed a Permutable feature fusion network for Drug-Drug Synergy prediction, named PermuteDDS. PermuteDDS takes multiple representations of drugs and cell lines as input and employs a permutable fusion mechanism to combine drug and cell line features. In experiments, PermuteDDS exhibits state-of-the-art performance on two benchmark data sets. Additionally, the results on independent test set grouped by different tissues reveal that PermuteDDS has good generalization performance. We believed that PermuteDDS is an effective and valuable tool for identifying synergistic drug combinations. It is publicly available at https://github.com/littlewei-lazy/PermuteDDS . SCIENTIFIC CONTRIBUTION First, this paper proposes a permutable feature fusion network for predicting drug synergy termed PermuteDDS, which extract diverse information from multiple drug representations and cell line representations. Second, the permutable fusion mechanism combine the drug and cell line features by integrating information of different channels, enabling the utilization of complex relationships between drugs and cell lines. Third, comparative and ablation experiments provide evidence of the efficacy of PermuteDDS in predicting drug-drug synergy.
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Affiliation(s)
- Xinwei Zhao
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Junqing Xu
- The Second Clinical Medical School, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Youyuan Shui
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Mengdie Xu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Jie Hu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China
| | - Xiaoyan Liu
- Faculty of Computing, Harbin Institute of Technology, No. 92 West Da Zhi St, Harbin, 150001, Heilongjiang, China
| | - Kai Che
- Xi'an Aeronautics Computing Technique Research Institute, AVIC, No. 156, TaiBai Nroth Road, Xi'an, 710068, Shanxi, China
- Aviation Key Laboratory of Science and Technology on Airborne and Missleborne Computer, Xi'an, 710065, Shanxi, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China.
| | - Yun Liu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China.
- Department of Information, the First Affiliated Hospital, Nanjing Medical University, No. 300 Guang Zhou Road, Nanjing, 210029, Jiangsu, China.
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22
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Wang T, Wang R, Wei L. AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction. J Chem Inf Model 2024; 64:2854-2862. [PMID: 37565997 DOI: 10.1021/acs.jcim.3c00709] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Identifying synergistic drug combinations is fundamentally important to treat a variety of complex diseases while avoiding severe adverse drug-drug interactions. Although several computational methods have been proposed, they highly rely on handcrafted feature engineering and cannot learn better interactive information between drug pairs, easily resulting in relatively low performance. Recently, deep-learning methods, especially graph neural networks, have been widely developed in this area and demonstrated their ability to address complex biological problems. In this study, we proposed AttenSyn, an attention-based deep graph neural network for accurately predicting synergistic drug combinations. In particular, we adopted a graph neural network module to extract high-latent features based on the molecular graphs only and exploited the attention-based pooling module to learn interactive information between drug pairs to strengthen the representations of drug pairs. Comparative results on the benchmark datasets demonstrated that our AttenSyn performs better than the state-of-the-art methods in the prediction of anticancer synergistic drug combinations. Additionally, to provide good interpretability of our model, we explored and visualized some crucial substructures in drugs through attention mechanisms. Furthermore, we also verified the effectiveness of our proposed AttenSyn on two cell lines by visualizing the features of drug combinations learnt from our model, exhibiting satisfactory generalization ability.
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Affiliation(s)
- Tianshuo Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
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23
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Rafiei F, Zeraati H, Abbasi K, Razzaghi P, Ghasemi JB, Parsaeian M, Masoudi-Nejad A. CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction. J Chem Inf Model 2024; 64:2577-2585. [PMID: 38514966 DOI: 10.1021/acs.jcim.3c01486] [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: 03/23/2024]
Abstract
Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.
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Affiliation(s)
- Fatemeh Rafiei
- Department of Epidemiology and Biostatistics, School of Health, Tehran University of Medical Sciences, Tehran 14167-53955, Iran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Health, Tehran University of Medical Sciences, Tehran 14167-53955, Iran
| | - Karim Abbasi
- Laboratory of System Biology, Bioinformatics & Artificial Intelligence in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran 14588-89694, Iran
| | - Parvin Razzaghi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Chemistry, School of Sciences, University of Tehran, Tehran 14174-66191, Iran
| | - Mahboubeh Parsaeian
- Department of Epidemiology and Biostatistics, School of Health, Tehran University of Medical Sciences, Tehran 14167-53955, Iran
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, U.K
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran 13145-1365, Iran
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24
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Dong Y, Chang Y, Wang Y, Han Q, Wen X, Yang Z, Zhang Y, Qiang Y, Wu K, Fan X, Ren X. MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction. BMC Bioinformatics 2024; 25:140. [PMID: 38561679 PMCID: PMC10985899 DOI: 10.1186/s12859-024-05765-y] [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: 01/17/2024] [Accepted: 03/28/2024] [Indexed: 04/04/2024] Open
Abstract
Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP .
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Affiliation(s)
- Yunyun Dong
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China.
| | - Yunqing Chang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yuxiang Wang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Qixuan Han
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xiaoyuan Wen
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Ziting Yang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yan Zhang
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yan Qiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, Shanxi, China.
| | - Kun Wu
- School of Computing, University of Leeds, Leeds, West Yorkshire, UK
| | - Xiaole Fan
- Information Management Department, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
| | - Xiaoqiang Ren
- Information Management Department, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
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25
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Verrelle P, Gestraud P, Poyer F, Soria A, Tessier S, Lescure A, Anthony E, Corbé M, Heinrich S, Beauvineau C, Chaput L, Granzhan A, Piguel S, Perez F, Teulade-Fichou MP, Megnin-Chanet F, Del Nery E. Integrated High-Throughput Screening and Large-Scale Isobolographic Analysis to Accelerate the Discovery of Radiosensitizers With Greater Selectivity for Cancer Cells. Int J Radiat Oncol Biol Phys 2024; 118:1294-1307. [PMID: 37778425 DOI: 10.1016/j.ijrobp.2023.09.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/03/2023]
Abstract
PURPOSE High-throughput screening (HTS) platforms have been widely used to identify candidate anticancer drugs and drug-drug combinations; however, HTS-based identification of new drug-ionizing radiation (IR) combinations has rarely been reported. Herein, we developed an integrated approach including cell-based HTS and computational large-scale isobolographic analysis to accelerate the identification of radiosensitizing compounds acting strongly and more specifically on cancer cells. METHODS AND MATERIALS In a 384-well plate format, 160 compounds likely to interfere with the cell response to radiation were screened on human glioblastoma (U251-MG) and cervix carcinoma (ME-180) cell lines, as well as on normal fibroblasts (CCD-19Lu). After drug exposure, cells were irradiated or not and short-term cell survival was assessed by high-throughput cell microscopy. Computational large-scale dose-response and isobolographic approach were used to identify promising synergistic drugs radiosensitizing cancer cells rather than normal cells. Synergy of a promising compound was confirmed on ME-180 cells by an independent 96-well assay protocol, and finally, by the gold-standard colony forming assay. RESULTS We retained 4 compounds synergistic at 2 isoeffects in U251-MG and ME-180 cell lines and 11 compounds synergistically effective in only one cancer cell line. Among these 15 promising radiosensitizers, 5 compounds showed limited toxicity combined or not with IR on normal fibroblasts. CONCLUSIONS Overall, this study demonstrated that HTS chemoradiation screening together with large-scale computational analysis is an efficient tool to identify synergistic drug-IR combinations, with concomitant assessment of unwanted toxicity on normal fibroblasts. It sparks expectations to accelerate the discovery of highly desired agents improving the therapeutic index of radiation therapy.
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Affiliation(s)
- Pierre Verrelle
- Radiation Oncology Department, Institut Curie Hospital, Paris, France; Chemistry and Modelisation for the Biology of Cancer, CNRS UMR9187, INSERM U1196, Institut Curie, Université Paris Saclay, 91405 Orsay, France.
| | - Pierre Gestraud
- Chemistry and Modelisation for the Biology of Cancer, CNRS UMR9187, INSERM U1196, Institut Curie, Université Paris Saclay, 91405 Orsay, France
| | - Florent Poyer
- Chemistry and Modelisation for the Biology of Cancer, CNRS UMR9187, INSERM U1196, Institut Curie, Université Paris Saclay, 91405 Orsay, France
| | - Adèle Soria
- Biophenics High-Content Screening Laboratory, Department of Translational Research, PSL Research University, PICT-IBiSa, Institut Curie Research Center, Paris, France
| | - Sarah Tessier
- Biophenics High-Content Screening Laboratory, Department of Translational Research, PSL Research University, PICT-IBiSa, Institut Curie Research Center, Paris, France
| | - Aurianne Lescure
- Biophenics High-Content Screening Laboratory, Department of Translational Research, PSL Research University, PICT-IBiSa, Institut Curie Research Center, Paris, France
| | - Elodie Anthony
- Biophenics High-Content Screening Laboratory, Department of Translational Research, PSL Research University, PICT-IBiSa, Institut Curie Research Center, Paris, France
| | - Maxime Corbé
- Biophenics High-Content Screening Laboratory, Department of Translational Research, PSL Research University, PICT-IBiSa, Institut Curie Research Center, Paris, France
| | - Sophie Heinrich
- Experimental Radiotherapy Platform (RadeXp), Translational Research Department, Institut Curie, Orsay, France; Inserm U1021-CNRS UMR 3347, Institut Curie, Paris Saclay University
| | - Claire Beauvineau
- Bioinformatics and Computational Systems Biology of Cancer, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
| | - Ludovic Chaput
- Bioinformatics and Computational Systems Biology of Cancer, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
| | - Anton Granzhan
- Bioinformatics and Computational Systems Biology of Cancer, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
| | - Sandrine Piguel
- Bioinformatics and Computational Systems Biology of Cancer, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France; BioCIS UMR8076, Université Paris-Saclay, Faculté de Pharmacie, Orsay, France
| | - Franck Perez
- Biophenics High-Content Screening Laboratory, Department of Translational Research, PSL Research University, PICT-IBiSa, Institut Curie Research Center, Paris, France; Cell Biology and Cancer UMR144, Institut Curie, PSL Research University, Paris, France
| | - Marie-Paule Teulade-Fichou
- Chemistry and Modelisation for the Biology of Cancer, CNRS UMR9187, INSERM U1196, Institut Curie, Université Paris Saclay, 91405 Orsay, France
| | - Frédérique Megnin-Chanet
- Bioinformatics and Computational Systems Biology of Cancer, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France
| | - Elaine Del Nery
- Biophenics High-Content Screening Laboratory, Department of Translational Research, PSL Research University, PICT-IBiSa, Institut Curie Research Center, Paris, France.
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26
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Yan S, Zheng D. A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer. Interdiscip Sci 2024; 16:218-230. [PMID: 38183569 DOI: 10.1007/s12539-023-00596-6] [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: 08/13/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/08/2024]
Abstract
The exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and chemical structure information for the prediction of synergistic effects in drug combinations. GTextSyn employs a sentence classification model within the domain of Natural Language Processing (NLP), wherein drugs and cell lines are regarded as entities possessing biochemical relevance. Meanwhile, combinations of drug pairs and cell lines are construed as sentences with biochemical relational significance. To assess the efficacy of GTextSyn, we conduct a comparative analysis with alternative deep learning approaches using a standard benchmark dataset. The results from a five-fold cross-validation demonstrate a 49.5% reduction in Mean Square Error (MSE) achieved by GTextSyn, surpassing the performance of the next best method in the regression task. Furthermore, we conduct a comprehensive literature survey on the predicted novel drug combinations and find substantial support from prior experimental studies for many of the combinations identified by GTextSyn.
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Affiliation(s)
- Shiyu Yan
- School of Computer, University of South China, West Changsheng Road, Hengyang, 421001, Hunan, China.
| | - Ding Zheng
- School of Computer, University of South China, West Changsheng Road, Hengyang, 421001, Hunan, China
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27
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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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28
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Abd El-Hafeez T, Shams MY, Elshaier YAMM, Farghaly HM, Hassanien AE. Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs. Sci Rep 2024; 14:2428. [PMID: 38287066 PMCID: PMC10825182 DOI: 10.1038/s41598-024-52814-w] [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: 11/09/2023] [Accepted: 01/24/2024] [Indexed: 01/31/2024] Open
Abstract
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.
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Affiliation(s)
- Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt.
- Computer Science Unit, Deraya University, El-Minia, Egypt.
| | - Mahmoud Y Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Yaseen A M M Elshaier
- Department of Organic and Medicinal Chemistry, Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufia, Egypt
| | - Heba Mamdouh Farghaly
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt.
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29
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Guo Y, Hu H, Chen W, Yin H, Wu J, Hsieh CY, He Q, Cao J. SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction. Brief Bioinform 2024; 25:bbae015. [PMID: 38340091 PMCID: PMC10858681 DOI: 10.1093/bib/bbae015] [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: 09/24/2023] [Revised: 12/18/2023] [Indexed: 02/12/2024] Open
Abstract
Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.
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Affiliation(s)
- Yue Guo
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
| | - Haitao Hu
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Polytechnic Institute, Zhejiang University, 269 Shixiang Road,310000, Hangzhou, Zhejiang, China
| | - Wenbo Chen
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Polytechnic Institute, Zhejiang University, 269 Shixiang Road,310000, Hangzhou, Zhejiang, China
| | - Hao Yin
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Polytechnic Institute, Zhejiang University, 269 Shixiang Road,310000, Hangzhou, Zhejiang, China
| | - Jian Wu
- Second Affiliated Hospital School of Medicine, School of Public Health, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
| | - Chang-Yu Hsieh
- The Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, 291 Fucheng Road, 310018, Hangzhou, Zhejiang, China
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
| | - Qiaojun He
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education, 310020, Hangzhou, Zhejiang, China
- The Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, 291 Fucheng Road, 310018, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
| | - Ji Cao
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education, 310020, Hangzhou, Zhejiang, China
- The Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, 291 Fucheng Road, 310018, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
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30
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Han S, Lee JE, Kang S, So M, Jin H, Lee JH, Baek S, Jun H, Kim TY, Lee YS. Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis. Brief Bioinform 2024; 25:bbae035. [PMID: 38349059 PMCID: PMC10862655 DOI: 10.1093/bib/bbae035] [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: 11/07/2023] [Revised: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.
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Affiliation(s)
- Seokjin Han
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Ji Eun Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Seolhee Kang
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Minyoung So
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hee Jin
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Jang Ho Lee
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Sunghyeob Baek
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hyungjin Jun
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Tae Yong Kim
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Yun-Sil Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
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31
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Liu M, Srivastava G, Ramanujam J, Brylinski M. Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects. Sci Rep 2024; 14:1668. [PMID: 38238448 PMCID: PMC10796434 DOI: 10.1038/s41598-024-51940-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 01/11/2024] [Indexed: 01/22/2024] Open
Abstract
Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that ML models trained on the augmented data consistently achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.
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Affiliation(s)
- Mengmeng Liu
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Gopal Srivastava
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - J Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA.
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA.
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32
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Monem S, Hassanien AE, Abdel-Hamid AH. A multi-task learning model for predicting drugs combination synergy by analyzing drug-drug interactions and integrated multi-view graph data. Sci Rep 2023; 13:22463. [PMID: 38105262 PMCID: PMC10725868 DOI: 10.1038/s41598-023-48991-9] [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: 09/14/2023] [Accepted: 12/02/2023] [Indexed: 12/19/2023] Open
Abstract
This paper proposes a multi-task deep learning model for determining drug combination synergistic by simultaneously output synergy scores and synergy class labels. Initially, the two drugs are represented using a Simplified Molecular-Input Line-Entry (SMILE) system. Chemical structural features of the drugs are extracted from the SMILE using the RedKit package. Additionally, an improved Multi-view representation is proposed to extract graph-based drug features. Furthermore, the cancer cell line is represented by gene expression. Then, a three fully connected layers are learned to extract cancer cell line features. To investigate the impact of drug interactions on cell lines, the drug interaction features are extracted from a pretrained drugs interaction network and fed into an attention mechanism along with the cancer cell line features, resulting in the output of affected cancer cell line features. Subsequently, the drug and cell line features are concatenated and fed into an attention mechanism, which produces a two-feature representation for the two predicted tasks. The relationship between the two tasks is learned using the cross-stitch algorithm. Finally, each task feature is inputted into a fully connected subnetwork to predict the synergy score and synergy label. The proposed model 'MutliSyn' is evaluated using the O'Neil cancer dataset, comprising 38 unique drugs combined to form 22,737 drug combination pairs, tested on 39 cancer cell lines. For the synergy score, the model achieves a mean square error (MSE) of 219.14, a root mean square error (RMSE) of 14.75, and a Pearson score of 0.76. Regarding the synergy class label, the model achieves an area under the ROC curve (ROC-AUC) of 0.95, an area under the precision-recall curve (PR-AUC) of 0.85, precision of 0.93, kappa of 0.61, and accuracy of 0.90.
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Affiliation(s)
- Samar Monem
- Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef, 62521, Egypt.
- Scientific Research Group in Egypt (SRGE), , .
| | - Aboul Ella Hassanien
- Faculty of Computer and AI, Cairo University, Cairo, Egypt
- Scientific Research Group in Egypt (SRGE),
| | - Alaa H Abdel-Hamid
- Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef, 62521, Egypt
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33
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Zhang H, Kreis J, Schelhorn SE, Dahmen H, Grombacher T, Zühlsdorf M, Zenke FT, Guan Y. Mapping combinatorial drug effects to DNA damage response kinase inhibitors. Nat Commun 2023; 14:8310. [PMID: 38097586 PMCID: PMC10721915 DOI: 10.1038/s41467-023-44108-y] [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: 10/27/2021] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
One fundamental principle that underlies various cancer treatments, such as traditional chemotherapy and radiotherapy, involves the induction of catastrophic DNA damage, leading to the apoptosis of cancer cells. In our study, we conduct a comprehensive dose-response combination screening focused on inhibitors that target key kinases involved in the DNA damage response (DDR): ATR, ATM, and DNA-PK. This screening involves 87 anti-cancer agents, including six DDR inhibitors, and encompasses 62 different cell lines spanning 12 types of tumors, resulting in a total of 17,912 combination treatment experiments. Within these combinations, we analyze the most effective and synergistic drug pairs across all tested cell lines, considering the variations among cancers originating from different tissues. Our analysis reveals inhibitors of five DDR-related pathways (DNA topoisomerase, PLK1 kinase, p53-inducible ribonucleotide reductase, PARP, and cell cycle checkpoint proteins) that exhibit strong combinatorial efficacy and synergy when used alongside ATM/ATR/DNA-PK inhibitors.
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Affiliation(s)
- Hanrui Zhang
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | | | | | | | | | | | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
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34
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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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35
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Wang W, Yuan G, Wan S, Zheng Z, Liu D, Zhang H, Li J, Zhou Y, Wang X. A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs. Brief Bioinform 2023; 25:bbad522. [PMID: 38243692 PMCID: PMC10796255 DOI: 10.1093/bib/bbad522] [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: 09/02/2023] [Revised: 11/08/2023] [Accepted: 12/19/2023] [Indexed: 01/21/2024] Open
Abstract
Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.
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Affiliation(s)
- Wei Wang
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Gaolin Yuan
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Shitong Wan
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Ziwei Zheng
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Dong Liu
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Hongjun Zhang
- Hebi Instiute of Engineering and Technology, Henan Polytechnic University, 458030, China
| | - Juntao Li
- School of Mathematics and Information Science, Henan Normal University, 453007 Xinxiang, China
| | - Yun Zhou
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Xianfang Wang
- College of Computer Science and Technology Engineering, Henan Institute of Technology, 453000, China
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36
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Malyutina A, Tang J, Amiryousefi A. Resolving network clusters disparity based on dissimilarity measurements with nonmetric analysis of variance. iScience 2023; 26:108354. [PMID: 38026214 PMCID: PMC10663764 DOI: 10.1016/j.isci.2023.108354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 06/22/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Classic ANOVA (cA) tests the explanatory power of a partitioning on a set of objects. More fit for clusters proximity analysis, nonparametric ANOVA (npA) extends to a case where instead of the object values themselves, their mutual distances are available. However, extending the cA applicability, the metric conditions in npA are limiting. Based on the central limit theorem (CLT), here we introduce nonmetric ANOVA (nmA) that by relaxing the metric properties between objects, allows an ANOVA-like statistical testing of a network clusters disparity. We present a parametric test statistic which under the null hypothesis of no differences between the competing clusters means, follows an exact F-distribution. We apply our method on three diverse biological examples, discuss its parallel performance, and note the specific use of each method tailored by the inherent data properties. The R code is provided at github.com/AmiryousefiLab/nmANOVA.
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Affiliation(s)
- Alina Malyutina
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Ali Amiryousefi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
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37
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Liu M, Srivastava G, Ramanujam J, Brylinski M. Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects. RESEARCH SQUARE 2023:rs.3.rs-3481858. [PMID: 37961281 PMCID: PMC10635365 DOI: 10.21203/rs.3.rs-3481858/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8,798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that Random Forest and Gradient Boosting Trees models trained on the augmented data achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.
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38
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Mao B, Guo S. Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models. CANCER RESEARCH COMMUNICATIONS 2023; 3:2146-2157. [PMID: 37830749 PMCID: PMC10591909 DOI: 10.1158/2767-9764.crc-23-0243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/31/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
Drug combination therapy is a promising strategy for treating cancer; however, its efficacy and synergy require rigorous evaluation in preclinical studies before going to clinical trials. Existing methods have limited power to detect synergy in animal studies. Here, we introduce a novel approach to assess in vivo drug synergy with high sensitivity and low false discovery rate. It can accurately estimate combination index and synergy score under the Bliss independence model and the highest single agent (HSA) model without any assumption on tumor growth kinetics, study duration, data completeness and balance for tumor volume measurement. We show that our method can effectively validate in vitro drug synergy discovered from cell line assays in in vivo xenograft experiments, and can help to elucidate the mechanism of action for immune checkpoint inhibitors in syngeneic mouse models by combining an anti-PD-1 antibody and several tumor-infiltrating leukocytes depletion treatments. We provide a unified view of in vitro and in vivo synergy by presenting a parallelism between the fixed-dose in vitro and the 4-group in vivo combination studies, so they can be better designed, analyzed, and compared. We emphasize that combination index, when defined here via relative survival of tumor cells, is both dose and time dependent, and give guidelines on designing informative in vivo combination studies. We explain how to interpret and apply Bliss and HSA synergies. Finally, we provide an open-source software package named invivoSyn that enables automated analysis of in vivo synergy using our method and several other existing methods. SIGNIFICANCE This work presents a general solution to reliably determine in vivo drug synergy in single-dose 4-group animal combination studies.
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Affiliation(s)
- Binchen Mao
- Crown Bioscience Inc., Suzhou, Jiangsu, P.R. China
| | - Sheng Guo
- Crown Bioscience Inc., Suzhou, Jiangsu, P.R. China
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39
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Bertin P, Rector-Brooks J, Sharma D, Gaudelet T, Anighoro A, Gross T, Martínez-Peña F, Tang EL, Suraj MS, Regep C, Hayter JBR, Korablyov M, Valiante N, van der Sloot A, Tyers M, Roberts CES, Bronstein MM, Lairson LL, Taylor-King JP, Bengio Y. RECOVER identifies synergistic drug combinations in vitro through sequential model optimization. CELL REPORTS METHODS 2023; 3:100599. [PMID: 37797618 PMCID: PMC10626197 DOI: 10.1016/j.crmeth.2023.100599] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 10/07/2023]
Abstract
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased toward synergistic agents and results do not generalize out of distribution. During 5 rounds of experimentation, we employ sequential model optimization with a deep learning model to select drug combinations increasingly enriched for synergism and active against a cancer cell line-evaluating only ∼5% of the total search space. Moreover, we find that learned drug embeddings (using structural information) begin to reflect biological mechanisms. In silico benchmarking suggests search queries are ∼5-10× enriched for highly synergistic drug combinations by using sequential rounds of evaluation when compared with random selection or ∼3× when using a pretrained model.
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Affiliation(s)
- Paul Bertin
- Mila, the Quebec AI Institute, Montreal, QC, Canada
| | | | | | | | | | | | | | - Eileen L Tang
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
| | | | | | | | | | | | - Almer van der Sloot
- IRIC, Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada
| | - Mike Tyers
- Program in Molecular Medicine, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada
| | | | - Michael M Bronstein
- Relation Therapeutics, London, UK; Department of Computer Science, University of Oxford, Oxford, UK
| | - Luke L Lairson
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
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40
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Gu J, Bang D, Yi J, Lee S, Kim DK, Kim S. A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning. Brief Bioinform 2023; 24:bbad285. [PMID: 37544660 DOI: 10.1093/bib/bbad285] [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: 05/15/2023] [Revised: 07/05/2023] [Accepted: 07/21/2023] [Indexed: 08/08/2023] Open
Abstract
Combination therapies have brought significant advancements to the treatment of various diseases in the medical field. However, searching for effective drug combinations remains a major challenge due to the vast number of possible combinations. Biomedical knowledge graph (KG)-based methods have shown potential in predicting effective combinations for wide spectrum of diseases, but the lack of credible negative samples has limited the prediction performance of machine learning models. To address this issue, we propose a novel model-agnostic framework that leverages existing drug-drug interaction (DDI) data as a reliable negative dataset and employs supervised contrastive learning (SCL) to transform drug embedding vectors to be more suitable for drug combination prediction. We conducted extensive experiments using various network embedding algorithms, including random walk and graph neural networks, on a biomedical KG. Our framework significantly improved performance metrics compared to the baseline framework. We also provide embedding space visualizations and case studies that demonstrate the effectiveness of our approach. This work highlights the potential of using DDI data and SCL in finding tighter decision boundaries for predicting effective drug combinations.
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Affiliation(s)
- Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- AIGENDRUG Co., Ltd., 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Sangseon Lee
- Institute of Computer Technology Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Dong Kyu Kim
- PHARMGENSCIENCE Co., Ltd., 216, Dongjak-daero, 06554 Seoul, Republic of Korea
| | - Sun Kim
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Department of Computer Science and Engineering, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- AIGENDRUG Co., Ltd., 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Institute of Computer Technology, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
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41
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Ariey-Bonnet J, Berges R, Montero MP, Mouysset B, Piris P, Muller K, Pinna G, Failes TW, Arndt GM, Morando P, Baeza-Kallee N, Colin C, Chinot O, Braguer D, Morelli X, André N, Carré M, Tabouret E, Figarella-Branger D, Le Grand M, Pasquier E. Combination drug screen targeting glioblastoma core vulnerabilities reveals pharmacological synergisms. EBioMedicine 2023; 95:104752. [PMID: 37572644 PMCID: PMC10433015 DOI: 10.1016/j.ebiom.2023.104752] [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: 12/21/2022] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Pharmacological synergisms are an attractive anticancer strategy. However, with more than 5000 approved-drugs and compounds in clinical development, identifying synergistic treatments represents a major challenge. METHODS High-throughput screening was combined with target deconvolution and functional genomics to reveal targetable vulnerabilities in glioblastoma. The role of the top gene hit was investigated by RNA interference, transcriptomics and immunohistochemistry in glioblastoma patient samples. Drug combination screen using a custom-made library of 88 compounds in association with six inhibitors of the identified glioblastoma vulnerabilities was performed to unveil pharmacological synergisms. Glioblastoma 3D spheroid, organotypic ex vivo and syngeneic orthotopic mouse models were used to validate synergistic treatments. FINDINGS Nine targetable vulnerabilities were identified in glioblastoma and the top gene hit RRM1 was validated as an independent prognostic factor. The associations of CHK1/MEK and AURKA/BET inhibitors were identified as the most potent amongst 528 tested pairwise drug combinations and their efficacy was validated in 3D spheroid models. The high synergism of AURKA/BET dual inhibition was confirmed in ex vivo and in vivo glioblastoma models, without detectable toxicity. INTERPRETATION Our work provides strong pre-clinical evidence of the efficacy of AURKA/BET inhibitor combination in glioblastoma and opens new therapeutic avenues for this unmet medical need. Besides, we established the proof-of-concept of a stepwise approach aiming at exploiting drug poly-pharmacology to unveil druggable cancer vulnerabilities and to fast-track the identification of synergistic combinations against refractory cancers. FUNDING This study was funded by institutional grants and charities.
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Affiliation(s)
- Jérémy Ariey-Bonnet
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France
| | - Raphael Berges
- Aix Marseille Université, CNRS, UMR 7051, INP, Inst Neurophysiopathol, Marseille, France
| | - Marie-Pierre Montero
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France
| | - Baptiste Mouysset
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France
| | - Patricia Piris
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France
| | - Kevin Muller
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France
| | - Guillaume Pinna
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette F-91198, France
| | - Tim W Failes
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW 2052, Australia; ACRF Drug Discovery Centre for Childhood Cancer, Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Greg M Arndt
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW 2052, Australia; ACRF Drug Discovery Centre for Childhood Cancer, Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Philippe Morando
- Aix Marseille Université, CNRS, UMR 7051, INP, Inst Neurophysiopathol, Marseille, France
| | - Nathalie Baeza-Kallee
- Aix Marseille Université, CNRS, UMR 7051, INP, Inst Neurophysiopathol, Marseille, France
| | - Carole Colin
- Aix Marseille Université, CNRS, UMR 7051, INP, Inst Neurophysiopathol, Marseille, France
| | - Olivier Chinot
- Aix-Marseille University, Assistance Publique-Hopitaux de Marseille, Centre Hospitalo-Universitaire Timone, Service de Neuro-Oncologie, Marseille, France
| | - Diane Braguer
- Aix Marseille Université, CNRS, UMR 7051, INP, Inst Neurophysiopathol, Marseille, France
| | - Xavier Morelli
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France
| | - Nicolas André
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France; Pediatric Oncology and Hematology Department, Hôpital pour Enfant de La Timone, AP-HM, Marseille, France; Metronomics Global Health Initiative, Marseille 13385, France
| | - Manon Carré
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France
| | - Emeline Tabouret
- Aix Marseille Université, CNRS, UMR 7051, INP, Inst Neurophysiopathol, Marseille, France; Aix-Marseille University, Assistance Publique-Hopitaux de Marseille, Centre Hospitalo-Universitaire Timone, Service de Neuro-Oncologie, Marseille, France
| | | | - Marion Le Grand
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France.
| | - Eddy Pasquier
- Aix Marseille Université, Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli Calmettes, Centre de Recherche en Cancérologie de Marseille (CRCM), Marseille, France; Metronomics Global Health Initiative, Marseille 13385, France.
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42
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Dong Z, Zhang H, Chen Y, Payne PRO, Li F. Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling. Cancers (Basel) 2023; 15:4210. [PMID: 37686486 PMCID: PMC10486573 DOI: 10.3390/cancers15174210] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.
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Affiliation(s)
- Zehao Dong
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; (Z.D.); (Y.C.)
| | - Heming Zhang
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
| | - Yixin Chen
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; (Z.D.); (Y.C.)
| | - Philip R. O. Payne
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
| | - Fuhai Li
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
- Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA
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43
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Chapdelaine AG, Sun G. Challenges and Opportunities in Developing Targeted Therapies for Triple Negative Breast Cancer. Biomolecules 2023; 13:1207. [PMID: 37627272 PMCID: PMC10452226 DOI: 10.3390/biom13081207] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/26/2023] [Accepted: 07/29/2023] [Indexed: 08/27/2023] Open
Abstract
Triple negative breast cancer (TNBC) is a heterogeneous group of breast cancers characterized by their lack of estrogen receptors, progesterone receptors, and the HER2 receptor. They are more aggressive than other breast cancer subtypes, with a higher mean tumor size, higher tumor grade, the worst five-year overall survival, and the highest rates of recurrence and metastasis. Developing targeted therapies for TNBC has been a major challenge due to its heterogeneity, and its treatment still largely relies on surgery, radiation therapy, and chemotherapy. In this review article, we review the efforts in developing targeted therapies for TNBC, discuss insights gained from these efforts, and highlight potential opportunities going forward. Accumulating evidence supports TNBCs as multi-driver cancers, in which multiple oncogenic drivers promote cell proliferation and survival. In such multi-driver cancers, targeted therapies would require drug combinations that simultaneously block multiple oncogenic drivers. A strategy designed to generate mechanism-based combination targeted therapies for TNBC is discussed.
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Affiliation(s)
| | - Gongqin Sun
- Department of Cell and Molecular Biology, University of Rhode Island, Kingston, RI 02881, USA;
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44
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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: 20] [Impact Index Per Article: 20.0] [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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45
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Zhang H, Ek CH, Rattray M, Milo M. SynBa: improved estimation of drug combination synergies with uncertainty quantification. Bioinformatics 2023; 39:i121-i130. [PMID: 37387161 DOI: 10.1093/bioinformatics/btad240] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION There exists a range of different quantification frameworks to estimate the synergistic effect of drug combinations. The diversity and disagreement in estimates make it challenging to determine which combinations from a large drug screening should be proceeded with. Furthermore, the lack of accurate uncertainty quantification for those estimates precludes the choice of optimal drug combinations based on the most favourable synergistic effect. RESULTS In this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations, so that actionable decisions can be derived from the model outputs. The actionability is enabled by incorporating the Hill equation into SynBa, so that the parameters representing the potency and the efficacy can be preserved. Existing knowledge may be conveniently inserted due to the flexibility of the prior, as shown by the empirical Beta prior defined for the normalized maximal inhibition. Through experiments on large combination screenings and comparison against benchmark methods, we show that SynBa provides improved accuracy of dose-response predictions and better-calibrated uncertainty estimation for the parameters and the predictions. AVAILABILITY AND IMPLEMENTATION The code for SynBa is available at https://github.com/HaotingZhang1/SynBa. The datasets are publicly available (DOI of DREAM: 10.7303/syn4231880; DOI of the NCI-ALMANAC subset: 10.5281/zenodo.4135059).
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Affiliation(s)
- Haoting Zhang
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, United Kingdom
- Health Data Research UK, London NW1 2BE, United Kingdom
| | - Carl Henrik Ek
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, United Kingdom
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester M13 9PL, United Kingdom
- Alan Turing Institute, London NW1 2DB, United Kingdom
| | - Marta Milo
- Oncology Data Science, Oncology R&D AstraZeneca, Cambridge CB2 8PA, United Kingdom
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46
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Nair NU, Greninger P, Zhang X, Friedman AA, Amzallag A, Cortez E, Sahu AD, Lee JS, Dastur A, Egan RK, Murchie E, Ceribelli M, Crowther GS, Beck E, McClanaghan J, Klump-Thomas C, Boisvert JL, Damon LJ, Wilson KM, Ho J, Tam A, McKnight C, Michael S, Itkin Z, Garnett MJ, Engelman JA, Haber DA, Thomas CJ, Ruppin E, Benes CH. A landscape of response to drug combinations in non-small cell lung cancer. Nat Commun 2023; 14:3830. [PMID: 37380628 PMCID: PMC10307832 DOI: 10.1038/s41467-023-39528-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/14/2023] [Indexed: 06/30/2023] Open
Abstract
Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.
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Affiliation(s)
- Nishanth Ulhas Nair
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Xiaohu Zhang
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Adam A Friedman
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Arnaud Amzallag
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eliane Cortez
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Avinash Das Sahu
- University of New Mexico, Comprehensive Cancer Center, Albuquerque, NM, USA
| | - Joo Sang Lee
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, 16419, Republic of Korea
| | - Anahita Dastur
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Regina K Egan
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ellen Murchie
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Erin Beck
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | | | | | | | - Leah J Damon
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jeffrey Ho
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Angela Tam
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sam Michael
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Zina Itkin
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Mathew J Garnett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
| | | | - Daniel A Haber
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institute of Health, Rockville, MD, 20850, USA
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Cyril H Benes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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47
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Gross SM, Mohammadi F, Sanchez-Aguila C, Zhan PJ, Liby TA, Dane MA, Meyer AS, Heiser LM. Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects. Nat Commun 2023; 14:3450. [PMID: 37301933 PMCID: PMC10257663 DOI: 10.1038/s41467-023-39122-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.
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Affiliation(s)
- Sean M Gross
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Farnaz Mohammadi
- Department of Bioengineering, University of California, Los Angeles; Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, USA
| | - Crystal Sanchez-Aguila
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Paulina J Zhan
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Tiera A Liby
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Mark A Dane
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Aaron S Meyer
- Department of Bioengineering, University of California, Los Angeles; Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
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48
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Janizek JD, Dincer AB, Celik S, Chen H, Chen W, Naxerova K, Lee SI. Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models. Nat Biomed Eng 2023; 7:811-829. [PMID: 37127711 PMCID: PMC11149694 DOI: 10.1038/s41551-023-01034-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/01/2023] [Indexed: 05/03/2023]
Abstract
Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes. Here by using feature attribution methods, we show that the quality of the explanations can be increased by leveraging ensembles of explainable machine-learning models. We applied the approach to a dataset of 133 combinations of 46 anticancer drugs tested in ex vivo tumour samples from 285 patients with acute myeloid leukaemia and uncovered a haematopoietic-differentiation signature underlying drug combinations with therapeutic synergy. Ensembles of machine-learning models trained to predict drug combination synergies on the basis of gene-expression data may improve the feature attribution quality of complex machine-learning models.
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Affiliation(s)
- Joseph D Janizek
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Ayse B Dincer
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Safiye Celik
- Recursion Pharmaceuticals, Salt Lake City, UT, USA
| | - Hugh Chen
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - William Chen
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Kamila Naxerova
- Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Su-In Lee
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
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49
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Liu H, Fan Z, Lin J, Yang Y, Ran T, Chen H. The recent progress of deep-learning-based in silico prediction of drug combination. Drug Discov Today 2023:103625. [PMID: 37236526 DOI: 10.1016/j.drudis.2023.103625] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/24/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023]
Abstract
Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.
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Affiliation(s)
- Haoyang Liu
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; College of Life Sciences, Nankai University, Tianjin 300071, China
| | - Zhiguang Fan
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Jie Lin
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China.
| | - Ting Ran
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China.
| | - Hongming Chen
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China.
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50
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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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