1
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Jiang Z, Gong Z, Dai X, Zhang H, Ding P, Shen C. Deep graph contrastive learning model for drug-drug interaction prediction. PLoS One 2024; 19:e0304798. [PMID: 38885206 PMCID: PMC11182529 DOI: 10.1371/journal.pone.0304798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024] Open
Abstract
Drug-drug interaction (DDI) is the combined effects of multiple drugs taken together, which can either enhance or reduce each other's efficacy. Thus, drug interaction analysis plays an important role in improving treatment effectiveness and patient safety. It has become a new challenge to use computational methods to accelerate drug interaction time and reduce its cost-effectiveness. The existing methods often do not fully explore the relationship between the structural information and the functional information of drug molecules, resulting in low prediction accuracy for drug interactions, poor generalization, and other issues. In this paper, we propose a novel method, which is a deep graph contrastive learning model for drug-drug interaction prediction (DeepGCL for brevity). DeepGCL incorporates a contrastive learning component to enhance the consistency of information between different views (molecular structure and interaction network), which means that the DeepGCL model predicts drug interactions by integrating molecular structure features and interaction network topology features. Experimental results show that DeepGCL achieves better performance than other methods in all datasets. Moreover, we conducted many experiments to analyze the necessity of each component of the model and the robustness of the model, which also showed promising results. The source code of DeepGCL is freely available at https://github.com/jzysj/DeepGCL.
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Affiliation(s)
- Zhenyu Jiang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
| | - Zhi Gong
- School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, China
- Key Laboratory of Intelligent Perception and Computing, Hunan University of Information Technology, Changsha, China
| | - Xiaopeng Dai
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
- School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, China
- Key Laboratory of Intelligent Perception and Computing, Hunan University of Information Technology, Changsha, China
| | - Hongyan Zhang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, China
| | - Cong Shen
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
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2
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Wu L, Gao J, Zhang Y, Sui B, Wen Y, Wu Q, Liu K, He S, Bo X. A hybrid deep forest-based method for predicting synergistic drug combinations. CELL REPORTS METHODS 2023; 3:100411. [PMID: 36936075 PMCID: PMC10014304 DOI: 10.1016/j.crmeth.2023.100411] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/27/2022] [Accepted: 01/27/2023] [Indexed: 02/23/2023]
Abstract
Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.
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Affiliation(s)
- Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jie Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122, China
| | - Yixin Zhang
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Binsheng Sui
- School of Film, Xiamen University, Xiamen 361005, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Qingqiang Wu
- School of Film, Xiamen University, Xiamen 361005, China
| | - Kunhong Liu
- School of Film, Xiamen University, Xiamen 361005, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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3
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Zhou M, Ma Y, Chiang CC, Rock EC, Luker KE, Luker GD, Chen YC. High-Throughput Cellular Heterogeneity Analysis in Cell Migration at the Single-Cell Level. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2206754. [PMID: 36449634 PMCID: PMC9908848 DOI: 10.1002/smll.202206754] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Indexed: 06/17/2023]
Abstract
Cancer cell migration represents an essential step toward metastasis and cancer deaths. However, conventional drug discovery focuses on cytotoxic and growth-inhibiting compounds rather than inhibitors of migration. Drug screening assays generally measure the average response of many cells, masking distinct cell populations that drive metastasis and resist treatments. Here, this work presents a high-throughput microfluidic cell migration platform that coordinates robotic liquid handling and computer vision for rapidly quantifying individual cellular motility. Using this innovative technology, 172 compounds were tested and a surprisingly low correlation between migration and growth inhibition was found. Notably, many compounds were found to inhibit migration of most cells while leaving fast-moving subpopulations unaffected. This work further pinpoints synergistic drug combinations, including Bortezomib and Danirixin, to stop fast-moving cells. To explain the observed cell behaviors, single-cell morphological and molecular analysis were performed. These studies establish a novel technology to identify promising migration inhibitors for cancer treatment and relevant applications.
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Affiliation(s)
- Mengli Zhou
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
- Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yushu Ma
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
| | - Chun-Cheng Chiang
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
| | - Edwin C. Rock
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, PA 15260, USA
| | - Kathryn E. Luker
- Center for Molecular Imaging, Department of Radiology, University of Michigan, 109 Zina Pitcher Place, Ann Arbor, MI 48109-2200, USA
| | - Gary D. Luker
- Center for Molecular Imaging, Department of Radiology, University of Michigan, 109 Zina Pitcher Place, Ann Arbor, MI 48109-2200, USA
- Department of Microbiology and Immunology, University of Michigan, 109 Zina Pitcher Place, Ann Arbor, MI 48109-2200, USA
- Department of Biomedical Engineering, University of Michigan, 2200 Bonisteel, Blvd. Ann Arbor, MI 48109-2099, USA
| | - Yu-Chih Chen
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, PA 15260, USA
- CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
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4
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Hosseini SR, Zhou X. CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy. Brief Bioinform 2023; 24:bbac588. [PMID: 36562722 PMCID: PMC9851301 DOI: 10.1093/bib/bbac588] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context aware and integrative deep-learning framework that we have established to unleash the potential of the Chemical Checker extended drug bioactivity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we next exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.
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Affiliation(s)
- Sayed-Rzgar Hosseini
- School of Biomedical Informatics, University of Texas Health Science Center (UTHealth), Houston, TX, USA
| | - Xiaobo Zhou
- School of Biomedical Informatics, University of Texas Health Science Center (UTHealth), Houston, TX, USA
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5
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Yue R, Dutta A. Computational systems biology in disease modeling and control, review and perspectives. NPJ Syst Biol Appl 2022; 8:37. [PMID: 36192551 PMCID: PMC9528884 DOI: 10.1038/s41540-022-00247-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 09/05/2022] [Indexed: 02/02/2023] Open
Abstract
Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.
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Affiliation(s)
- Rongting Yue
- Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, Storrs, CT, 06269, USA.
| | - Abhishek Dutta
- Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, Storrs, CT, 06269, USA
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6
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Pantaleón García J, Kulkarni VV, Reese TC, Wali S, Wase SJ, Zhang J, Singh R, Caetano MS, Kadara H, Moghaddam S, Johnson FM, Wang J, Wang Y, Evans S. OBIF: an omics-based interaction framework to reveal molecular drivers of synergy. NAR Genom Bioinform 2022; 4:lqac028. [PMID: 35387383 PMCID: PMC8982434 DOI: 10.1093/nargab/lqac028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/28/2022] [Accepted: 03/10/2022] [Indexed: 01/08/2023] Open
Abstract
Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug–drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.
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Affiliation(s)
- Jezreel Pantaleón García
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
| | - Vikram V Kulkarni
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Tanner C Reese
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- Rice University, Houston, TX 77005, USA
| | - Shradha Wali
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Saima J Wase
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
| | - Jiexin Zhang
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ratnakar Singh
- Department of Thoracic, Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Comparative Biosciences, University of Illinois at Urbana-Champaign, Urbana, IL 61802, USA
| | - Mauricio S Caetano
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
| | - Humam Kadara
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Seyed Javad Moghaddam
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Faye M Johnson
- Department of Thoracic, Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jing Wang
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yongxing Wang
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
| | - Scott E Evans
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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7
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Network Biology and Artificial Intelligence Drive the Understanding of the Multidrug Resistance Phenotype in Cancer. Drug Resist Updat 2022; 60:100811. [DOI: 10.1016/j.drup.2022.100811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/22/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023]
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8
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Güvenç Paltun B, Kaski S, Mamitsuka H. Machine learning approaches for drug combination therapies. Brief Bioinform 2021; 22:bbab293. [PMID: 34368832 PMCID: PMC8574999 DOI: 10.1093/bib/bbab293] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/08/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.
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Affiliation(s)
- Betül Güvenç Paltun
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
| | - Samuel Kaski
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
- University of Manchester, UK
| | - Hiroshi Mamitsuka
- Department of Computer Science, Aalto University, Espoo, Finland
- Helsinki Institute for Information Technology (HIIT), Finland
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 6110011, Japan
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9
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Wu L, Wen Y, Leng D, Zhang Q, Dai C, Wang Z, Liu Z, Yan B, Zhang Y, Wang J, He S, Bo X. Machine learning methods, databases and tools for drug combination prediction. Brief Bioinform 2021; 23:6363058. [PMID: 34477201 PMCID: PMC8769702 DOI: 10.1093/bib/bbab355] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.
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Affiliation(s)
- Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yuqi Wen
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Dongjin Leng
- Beijing Institute of Radiation Medicine, Beijing, China
| | | | - Chong Dai
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Zhongming Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Ziqi Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, AMMS, Beijing, China
| | - Bowei Yan
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Yixin Zhang
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Jing Wang
- School of Medicine, Tsinghua University, Beijing, China
| | - Song He
- Beijing Institute of Radiation Medicine, Beijing, China
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing, China
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10
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Wang B, Warden AR, Ding X. The optimization of combinatorial drug therapies: Strategies and laboratorial platforms. Drug Discov Today 2021; 26:2646-2659. [PMID: 34332097 DOI: 10.1016/j.drudis.2021.07.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/19/2021] [Accepted: 07/14/2021] [Indexed: 12/26/2022]
Abstract
Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.
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Affiliation(s)
- Boqian Wang
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China
| | - Antony R Warden
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China
| | - Xianting Ding
- Institute for Personalized Medicine, State Key Laboratory of Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, PR China.
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11
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Costa A, Vale N. Strategies for the treatment of breast cancer: from classical drugs to mathematical models. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:6328-6385. [PMID: 34517536 DOI: 10.3934/mbe.2021316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Breast cancer is one of the most common cancers and generally affects women. It is a heterogeneous disease that presents different entities, different biological characteristics, and differentiated clinical behaviors. With this in mind, this literature review had as its main objective to analyze the path taken from the simple use of classical drugs to the application of mathematical models, which through the many ongoing studies, have been considered as one of the reliable strategies, explaining the reasons why chemotherapy is not always successful. Besides, the most commonly mentioned strategies are immunotherapy, which includes techniques and therapies such as the use of antibodies, cytokines, antitumor vaccines, oncolytic and genomic viruses, among others, and nanoparticles, including metallic, magnetic, polymeric, liposome, dendrimer, micelle, and others, as well as drug reuse, which is a process by which new therapeutic indications are found for existing and approved drugs. The most commonly used pharmacological categories are cardiac, antiparasitic, anthelmintic, antiviral, antibiotic, and others. For the efficient development of reused drugs, there must be a process of exchange of purposes, methods, and information already available, and for their better understanding, computational mathematical models are then used, of which the methods of blind search or screening, based on the target, knowledge, signature, pathway or network and the mechanism to which it is directed, stand out. To conclude it should be noted that these different strategies can be applied alone or in combination with each other always to improve breast cancer treatment.
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Affiliation(s)
- Ana Costa
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal
| | - Nuno Vale
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal
- Department of Community Medicine, Health Information and Decision (MEDCIDS), Faculty of Medicine, University of Porto, Al. Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
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12
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Malinzi J, Basita KB, Padidar S, Adeola HA. Prospect for application of mathematical models in combination cancer treatments. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
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13
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The Crosstalk between FAK and Wnt Signaling Pathways in Cancer and Its Therapeutic Implication. Int J Mol Sci 2020; 21:ijms21239107. [PMID: 33266025 PMCID: PMC7730291 DOI: 10.3390/ijms21239107] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 11/24/2020] [Accepted: 11/26/2020] [Indexed: 12/12/2022] Open
Abstract
Focal adhesion kinase (FAK) and Wnt signaling pathways are important contributors to tumorigenesis in several cancers. While most results come from studies investigating these pathways individually, there is increasing evidence of a functional crosstalk between both signaling pathways during development and tumor progression. A number of FAK-Wnt interactions are described, suggesting an intricate, context-specific, and cell type-dependent relationship. During development for instance, FAK acts mainly upstream of Wnt signaling; and although in intestinal homeostasis and mucosal regeneration Wnt seems to function upstream of FAK signaling, FAK activates the Wnt/β-catenin signaling pathway during APC-driven intestinal tumorigenesis. In breast, lung, and pancreatic cancers, FAK is reported to modulate the Wnt signaling pathway, while in prostate cancer, FAK is downstream of Wnt. In malignant mesothelioma, FAK and Wnt show an antagonistic relationship: Inhibiting FAK signaling activates the Wnt pathway and vice versa. As the identification of effective Wnt inhibitors to translate in the clinical setting remains an outstanding challenge, further understanding of the functional interaction between Wnt and FAK could reveal new therapeutic opportunities and approaches greatly needed in clinical oncology. In this review, we summarize some of the most relevant interactions between FAK and Wnt in different cancers, address the current landscape of Wnt- and FAK-targeted therapies in different clinical trials, and discuss the rationale for targeting the FAK-Wnt crosstalk, along with the possible translational implications.
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14
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Niederdorfer B, Touré V, Vazquez M, Thommesen L, Kuiper M, Lægreid A, Flobak Å. Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction. Front Physiol 2020; 11:862. [PMID: 32848834 PMCID: PMC7399174 DOI: 10.3389/fphys.2020.00862] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 06/26/2020] [Indexed: 12/16/2022] Open
Abstract
Discrete dynamical modeling shows promise in prioritizing drug combinations for screening efforts by reducing the experimental workload inherent to the vast numbers of possible drug combinations. We have investigated approaches to predict combination responses across different cancer cell lines using logic models generated from one generic prior-knowledge network representing 144 nodes covering major cancer signaling pathways. Cell-line specific models were configured to agree with baseline activity data from each unperturbed cell line. Testing against experimental data demonstrated a high number of true positive and true negative predictions, including also cell-specific responses. We demonstrate the possible enhancement of predictive capability of models by curation of literature knowledge further detailing subtle biologically founded signaling mechanisms in the model topology. In silico model analysis pinpointed a subset of network nodes highly influencing model predictions. Our results indicate that the performance of logic models can be improved by focusing on high-influence node protein activity data for model configuration and that these nodes accommodate high information flow in the regulatory network.
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Affiliation(s)
- Barbara Niederdorfer
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Miguel Vazquez
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.,Barcelona Supercomputing Center, Barcelona, Spain
| | - Liv Thommesen
- Department of Biomedical Laboratory Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Astrid Lægreid
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.,The Cancer Clinic, St. Olav's University Hospital, Trondheim, Norway
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15
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Huang L, Brunell D, Stephan C, Mancuso J, Yu X, He B, Thompson TC, Zinner R, Kim J, Davies P, Wong STC. Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction. Bioinformatics 2020; 35:3709-3717. [PMID: 30768150 PMCID: PMC6761967 DOI: 10.1093/bioinformatics/btz109] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 12/21/2018] [Accepted: 02/13/2019] [Indexed: 12/31/2022] Open
Abstract
Motivation Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data. Results This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans. Availability and implementation DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lei Huang
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA.,Houston Methodist Hospital, Houston Methodist Cancer Center, Houston, TX, USA
| | - David Brunell
- Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA
| | - Clifford Stephan
- Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA
| | - James Mancuso
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA
| | - Xiaohui Yu
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA
| | - Bin He
- Department of Urology, Immunobiology & Transplant Science Center, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, USA.,Department of Medicine, Weill Cornell Medicine of Cornell University, New York, NY, USA
| | - Timothy C Thompson
- Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ralph Zinner
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Jeri Kim
- Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter Davies
- Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA
| | - Stephen T C Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medicine of Cornell University, Houston, TX, USA.,Houston Methodist Hospital, Houston Methodist Cancer Center, Houston, TX, USA
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16
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Liu H, Zhang W, Zou B, Wang J, Deng Y, Deng L. DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy. Nucleic Acids Res 2020; 48:D871-D881. [PMID: 31665429 PMCID: PMC7145671 DOI: 10.1093/nar/gkz1007] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/14/2019] [Accepted: 10/17/2019] [Indexed: 01/09/2023] Open
Abstract
Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.
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Affiliation(s)
- Hui Liu
- Lab of Information Management, Changzhou University, Changzhou 213164, China
| | - Wenhao Zhang
- Lab of Information Management, Changzhou University, Changzhou 213164, China
| | - Bo Zou
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Jinxian Wang
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Yuanyuan Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China.,School of Software, Xinjiang University, Urumqi 830008, China
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17
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Huang HJ, Kraevaya OA, Voronov II, Troshin PA, Hsu SH. Fullerene Derivatives as Lung Cancer Cell Inhibitors: Investigation of Potential Descriptors Using QSAR Approaches. Int J Nanomedicine 2020; 15:2485-2499. [PMID: 32368036 PMCID: PMC7170710 DOI: 10.2147/ijn.s243463] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 03/03/2020] [Indexed: 12/02/2022] Open
Abstract
Background Nanotechnology-based strategies in the treatment of cancer have potential advantages because of the favorable delivery of nanoparticles into tumors through porous vasculature. Materials and Methods In the current study, we synthesized a series of water-soluble fullerene derivatives and observed their anti-tumor effects on human lung carcinoma A549 cell lines. The quantitative structure–activity relationship (QSAR) modeling was employed to investigate the relationship between anticancer effects and descriptors relevant to peculiarities of molecular structures of fullerene derivatives. Results In the QSAR regression model, the evaluation results revealed that the determination coefficient r2 and leave-one-out cross-validation q2 for the recommended QSAR model were 0.9966 and 0.9246, respectively, indicating the reliability of the results. The molecular modeling showed that the lack of chlorine atom and a lower number of aliphatic single bonds in saturated hydrocarbon chains may be positively correlated with the lung cancer cytotoxicity of fullerene derivatives. Synthesized water-soluble fullerene derivatives have potential functional groups to inhibit the proliferation of lung cancer cells. Conclusion The guidelines obtained from the QSAR model might strongly facilitate the rational design of potential fullerene-based drug candidates for lung cancer therapy in the future.
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Affiliation(s)
- Hung-Jin Huang
- Institute of Polymer Science and Engineering, National Taiwan University, Taipei, Taiwan.,Institute of Cellular and System Medicine, National Health Research Institutes, Miaoli, Taiwan
| | - Olga A Kraevaya
- Skolkovo Institute of Science and Technology, Moscow, Russian Federation.,Institute for Problems of Chemical Physics of Russian Academy of Sciences, Chernogolovka, Russian Federation
| | - Ilya I Voronov
- Institute for Problems of Chemical Physics of Russian Academy of Sciences, Chernogolovka, Russian Federation
| | - Pavel A Troshin
- Skolkovo Institute of Science and Technology, Moscow, Russian Federation.,Institute for Problems of Chemical Physics of Russian Academy of Sciences, Chernogolovka, Russian Federation
| | - Shan-Hui Hsu
- Institute of Polymer Science and Engineering, National Taiwan University, Taipei, Taiwan.,Institute of Cellular and System Medicine, National Health Research Institutes, Miaoli, Taiwan.,Research and Development Center for Medical Devices, National Taiwan University, Taipei, Taiwan
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18
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Molecular Complexity of Diffuse Large B-Cell Lymphoma: Can It Be a Roadmap for Precision Medicine? Cancers (Basel) 2020; 12:cancers12010185. [PMID: 31940809 PMCID: PMC7017344 DOI: 10.3390/cancers12010185] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 01/07/2020] [Accepted: 01/09/2020] [Indexed: 12/11/2022] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma; it features extreme molecular heterogeneity regardless of the classical cell-of-origin (COO) classification. Despite this, the standard therapeutic approach is still immunochemotherapy (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone-R-CHOP), which allows a 60% overall survival (OS) rate, but up to 40% of patients experience relapse or refractory (R/R) disease. With the purpose of searching for new clinical parameters and biomarkers helping to make a better DLBCL patient characterization and stratification, in the last years a series of large discovery genomic and transcriptomic studies has been conducted, generating a wealth of information that needs to be put in order. We reviewed these researches, trying ultimately to understand if there are bases offering a roadmap toward personalized and precision medicine also for DLBCL.
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19
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Bertsimas D, Zhuo YD. Novel Target Discovery of Existing Therapies: Path to Personalized Cancer Therapy. ACTA ACUST UNITED AC 2020. [DOI: 10.1287/ijoo.2019.0019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Dimitris Bertsimas
- Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Ying Daisy Zhuo
- Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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20
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Liu H, Zhang W, Nie L, Ding X, Luo J, Zou L. Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network. BMC Bioinformatics 2019; 20:645. [PMID: 31818267 PMCID: PMC6902475 DOI: 10.1186/s12859-019-3288-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 11/21/2019] [Indexed: 01/30/2023] Open
Abstract
Background Although targeted drugs have contributed to impressive advances in the treatment of cancer patients, their clinical benefits on tumor therapies are greatly limited due to intrinsic and acquired resistance of cancer cells against such drugs. Drug combinations synergistically interfere with protein networks to inhibit the activity level of carcinogenic genes more effectively, and therefore play an increasingly important role in the treatment of complex disease. Results In this paper, we combined the drug similarity network, protein similarity network and known drug-protein associations into a drug-protein heterogenous network. Next, we ran random walk with restart (RWR) on the heterogenous network using the combinatorial drug targets as the initial probability, and obtained the converged probability distribution as the feature vector of each drug combination. Taking these feature vectors as input, we trained a gradient tree boosting (GTB) classifier to predict new drug combinations. We conducted performance evaluation on the widely used drug combination data set derived from the DCDB database. The experimental results show that our method outperforms seven typical classifiers and traditional boosting algorithms. Conclusions The heterogeneous network-derived features introduced in our method are more informative and enriching compared to the primary ontology features, which results in better performance. In addition, from the perspective of network pharmacology, our method effectively exploits the topological attributes and interactions of drug targets in the overall biological network, which proves to be a systematic and reliable approach for drug discovery.
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Affiliation(s)
- Hui Liu
- Lab of Information Management, Changzhou University, Jiangsu, China
| | - Wenhao Zhang
- Lab of Information Management, Changzhou University, Jiangsu, China
| | - Lixia Nie
- School of Information Science and Engineering, Changzhou University, Jiangsu, China
| | - Xiancheng Ding
- Information Center, Changzhou University, Jiangsu, 213164, China
| | - Judong Luo
- Department of Radiation Oncology, the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, China.
| | - Ling Zou
- School of Information Science and Engineering, Changzhou University, Jiangsu, China.
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21
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Prabaharan CB, Yang AB, Chidambaram D, Rajamanickam K, Napper S, Sakharkar MK. Ibrutinib as a potential therapeutic option for HER2 overexpressing breast cancer - the role of STAT3 and p21. Invest New Drugs 2019; 38:909-921. [PMID: 31375978 DOI: 10.1007/s10637-019-00837-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 07/11/2019] [Indexed: 10/26/2022]
Abstract
Treatment response rates to current anticancer therapies for HER2 overexpressing breast cancer are limited and are associated with severe adverse drug reactions. Tyrosine kinases perform crucial roles in cellular processes by mediating cell signalling cascades. Ibrutinib is a recently approved Tyrosine Kinase Inhibitor (TKI) that has been shown be an effective therapeutic option for HER2 overexpressing breast cancer. The molecular mechanisms, pathways, or genes that are modulated by ibrutinib and the mechanism of action of ibrutinib in HER2 overexpressing breast cancer remain obscure. In this study, we have performed a kinome array analysis of ibrutinib treatment in two HER2 overexpressing breast cancer cell lines. Our analysis shows that ibrutinib induces changes in nuclear morphology and causes apoptosis via caspase-dependent extrinsic apoptosis pathway with the activation of caspases-8, caspase-3, and cleavage of PARP1. We further show that phosphorylated STAT3Y705 is upregulated and phosphorylated p21T145 is downregulated upon ibrutinib treatment. We propose that STAT3 upregulation is a passive response as a result of induction of DNA damage and downregulation of phosphorylated p21 is promoting cell cycle arrest and apoptosis in the two HER2 overexpressing cell lines. These results suggest that inhibitors of STAT3 phosphorylation may be potential options for combination therapy to help increase the efficacy of ibrutinib against HER2-overexpressing tumors.
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Affiliation(s)
- Chandra Bose Prabaharan
- College of Pharmacy and Nutrition, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada
| | - Allan Boyao Yang
- Department of Anatomy, Physiology and Pharmacology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada
| | - Divya Chidambaram
- College of Pharmacy and Nutrition, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada
| | - Karthic Rajamanickam
- College of Pharmacy and Nutrition, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada
| | - Scott Napper
- Vaccine and Infectious Disease Organization-International Vaccine Research Centre, University of Saskatchewan, 120 Veterinary Road, Saskatoon, SK, S7N 5E3, Canada.,Department of Biochemistry, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada
| | - Meena Kishore Sakharkar
- College of Pharmacy and Nutrition, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada.
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22
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Kim IW, Kim JH, Oh JM. Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer. Front Oncol 2019; 9:661. [PMID: 31396486 PMCID: PMC6664029 DOI: 10.3389/fonc.2019.00661] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 07/05/2019] [Indexed: 12/20/2022] Open
Abstract
Purpose: Most prostate cancers (PCs) initially respond to androgen deprivation therapy (ADT), but eventually many PC patients develop castration resistant PC (CRPC). Currently, available drugs that have been approved for the treatment of CRPC patients are limited. Computational drug repositioning methods using public databases represent a promising and efficient tool for discovering new uses for existing drugs. The purpose of the present study is to predict drug candidates that can treat CRPC using a computational method that integrates publicly available gene expression data of tumors from CRPC patients, drug-induced gene expression data and drug response activity data. Methods: Gene expression data from tumoral and normal or benign prostate tissue samples in CRPC patients were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in CRPC were determined with a meta-signature analysis by a metaDE R package. Additionally, drug activity data were downloaded from the ChEMBL database. Furthermore, the drug-induced gene expression data were downloaded from the LINCS database. The reversal relationship between the CRPC and drug gene expression signatures as the Reverse Gene Expression Scores (RGES) were computed. Drug candidates to treat CRPC were predicted using summarized scores (sRGES). Additionally, synergic effects of drug combinations were predicted with a Target Inhibition interaction using the Minimization and Maximization Averaging (TIMMA) algorithm. Results: The drug candidates of sorafenib, olaparib, elesclomol, tanespimycin, and ponatinib were predicted to be active for the treatment of CRPC. Meanwhile, CRPC-related genes, in this case MYL9, E2F2, APOE, and ZFP36, were identified as having gene expression data that can be reversed by these drugs. Additionally, lenalidomide in combination with pazopanib was predicted to be most potent for CRPC. Conclusion: These findings support the use of a computational reversal gene expression approach to identify new drug and drug combination candidates that can be used to treat CRPC.
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Affiliation(s)
- In-Wha Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul, South Korea
| | - Jae Hyun Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul, South Korea
| | - Jung Mi Oh
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul, South Korea
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23
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Zhang W, Huai Y, Miao Z, Qian A, Wang Y. Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery. Front Pharmacol 2019; 10:743. [PMID: 31379563 PMCID: PMC6657703 DOI: 10.3389/fphar.2019.00743] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 06/07/2019] [Indexed: 01/01/2023] Open
Abstract
As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.
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Affiliation(s)
- Wenjuan Zhang
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Ying Huai
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Zhiping Miao
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Airong Qian
- Lab for Bone Metabolism, Key Lab for Space Biosciences and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- Research Center for Special Medicine and Health Systems Engineering, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
- NPU-UAB Joint Laboratory for Bone Metabolism, School of Life Sciences, Northwestern Polytechnical University, Xi’an, China
| | - Yonghua Wang
- Lab of Systems Pharmacology, College of Life Sciences, Northwest University, Xi’an, China
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24
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Tang J, Gautam P, Gupta A, He L, Timonen S, Akimov Y, Wang W, Szwajda A, Jaiswal A, Turei D, Yadav B, Kankainen M, Saarela J, Saez-Rodriguez J, Wennerberg K, Aittokallio T. Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer. NPJ Syst Biol Appl 2019; 5:20. [PMID: 31312514 PMCID: PMC6614366 DOI: 10.1038/s41540-019-0098-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 06/06/2019] [Indexed: 01/02/2023] Open
Abstract
Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.
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Affiliation(s)
- Jing Tang
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Prson Gautam
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Abhishekh Gupta
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, CT USA
| | - Liye He
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Sanna Timonen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Yevhen Akimov
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Wenyu Wang
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Agnieszka Szwajda
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Alok Jaiswal
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Denes Turei
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Bhagwan Yadav
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Hematology Research Unit Helsinki, Department of Medicine and Clinical Chemistry, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Matti Kankainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jani Saarela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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25
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Sheng Z, Sun Y, Yin Z, Tang K, Cao Z. Advances in computational approaches in identifying synergistic drug combinations. Brief Bioinform 2019; 19:1172-1182. [PMID: 28475767 DOI: 10.1093/bib/bbx047] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Indexed: 12/21/2022] Open
Abstract
Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.
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Affiliation(s)
- Zhen Sheng
- School of Life Sciences and Technology, Tongji University
| | - Yi Sun
- School of Life Sciences and Technology, Tongji University
| | - Zuojing Yin
- School of Life Sciences and Technology, Tongji University
| | - Kailin Tang
- Advanced Institute of Translational Medicine, Tongji University
| | - Zhiwei Cao
- School of Life Sciences and Technology, Tongji University
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26
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Naveja JJ, Stumpfe D, Medina-Franco JL, Bajorath J. Exploration of Target Synergy in Cancer Treatment by Cell-Based Screening Assay and Network Propagation Analysis. J Chem Inf Model 2019; 59:3072-3079. [PMID: 31013082 DOI: 10.1021/acs.jcim.9b00036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Computational approaches have previously been introduced to predict compounds with activity against multiple targets or compound combinations with synergistic functional effects. By contrast, there are no computational studies available that explore combinations of targets that might act synergistically upon small molecule treatment. Herein, we introduce an approach designed to identify synergistic target pairs on the basis of cell-based screening data and compounds with known target annotations. The targets involved in forming synergistic pairs were analyzed through a novel network propagation algorithm for rationalizing possible common synergy mechanisms. This algorithm enabled further analysis of each synergistic target pair and the identification of "interactors", i.e., proteins with higher propagation scores than would be expected by adding the individual contributions of each target in the synergistic pair. We detected 137 synergistic target pairs including 51 unique targets. A global network analysis of these 51 targets made it possible to derive a subnetwork of proteins with significant synergy. Furthermore, interactors were identified for 87 synergistic target pairs upon individual analysis of the network propagation of each pair. These interactors were associated with pathways related to cancer and apoptosis, membrane transport, and steroid metabolism and provided possible explanations of synergistic effects.
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Affiliation(s)
- J Jesús Naveja
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry , Rheinische Friedrich-Wilhelms-Universität , Endenicher Allee 19c , D-53115 Bonn , Germany.,PECEM, Faculty of Medicine , Universidad Nacional Autónoma de México , Mexico City , 04510 , Mexico.,Department of Pharmacy, School of Chemistry , Universidad Nacional Autónoma de México , Mexico City , 04510 , Mexico
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry , Rheinische Friedrich-Wilhelms-Universität , Endenicher Allee 19c , D-53115 Bonn , Germany
| | - José L Medina-Franco
- Department of Pharmacy, School of Chemistry , Universidad Nacional Autónoma de México , Mexico City , 04510 , Mexico
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry , Rheinische Friedrich-Wilhelms-Universität , Endenicher Allee 19c , D-53115 Bonn , Germany
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Tanoli Z, Alam Z, Ianevski A, Wennerberg K, Vähä-Koskela M, Aittokallio T. Interactive visual analysis of drug–target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing. Brief Bioinform 2018; 21:211-220. [PMID: 30566623 DOI: 10.1093/bib/bby119] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 11/01/2018] [Accepted: 11/19/2018] [Indexed: 12/13/2022] Open
Abstract
Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications. In this work, we demonstrate an integrated use of the rich bioactivity data from DTC and related drug databases using Drug Target Profiler (DTP), an open-source software and web tool for interactive exploration of drug-target interaction networks. DTP was designed for network-centric modeling of mode-of-action of multi-targeting anticancer compounds, especially for precision oncology applications. DTP enables users to construct an interaction network based on integrated bioactivity data across selected chemical compounds and their protein targets, further customizable using various visualization and filtering options, as well as cross-links to several drug and protein databases to provide comprehensive information of the network nodes and interactions. We demonstrate here the operation of the DTP tool and its unique features by several use cases related to both drug discovery and drug repurposing applications, using examples of anticancer drugs with shared target profiles. DTP is freely accessible at http://drugtargetprofiler.fimm.fi/.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Zaid Alam
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Aleksandr Ianevski
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology, Aalto University, Espoo, Finland
| | | | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology, Aalto University, Espoo, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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Zhang Z, Chen YC, Urs S, Chen L, Simeone DM, Yoon E. Scalable Multiplexed Drug-Combination Screening Platforms Using 3D Microtumor Model for Precision Medicine. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2018; 14:e1703617. [PMID: 30239130 DOI: 10.1002/smll.201703617] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 04/12/2018] [Indexed: 05/15/2023]
Abstract
Cancer heterogeneity is a notorious hallmark of this disease, and it is desirable to tailor effective treatments for each individual patient. Drug combinations have been widely accepted in cancer treatment for better therapeutic efficacy as compared to a single compound. However, experimental complexity and cost grow exponentially with more target compounds under investigation. The primary challenge remains to efficiently perform a large-scale drug combination screening using a small number of patient primary samples for testing. Here, a scalable, easy-to-use, high-throughput drug combination screening scheme is reported, which has the potential of screening all possible pairwise drug combinations for arbitrary number of drugs with multiple logarithmic mixing ratios. A "Christmas tree mixer" structure is introduced to generate a logarithmic concentration mixing ratio between drug pairs, providing a large drug concentration range for screening. A three-layer structure design and special inlets arrangement facilitate simple drug loading process. As a proof of concept, an 8-drug combination chip is implemented, which is capable of screening 172 different treatment conditions over 1032 3D cancer spheroids on a single chip. Using both cancer cell lines and patient-derived cancer cells, effective drug combination screening is demonstrated for precision medicine.
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Affiliation(s)
- Zhixiong Zhang
- Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI, 48109-2122, USA
| | - Yu-Chih Chen
- Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI, 48109-2122, USA
- University of Michigan Comprehensive Cancer Center, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Sumithra Urs
- University of Michigan Health System, Ann Arbor, MI, 48109, USA
| | - Lili Chen
- Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI, 48109-2122, USA
| | - Diane M Simeone
- University of Michigan Health System, Ann Arbor, MI, 48109, USA
| | - Euisik Yoon
- Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI, 48109-2122, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology 2018; 287:732-747. [PMID: 29782246 DOI: 10.1148/radiol.2018172171] [Citation(s) in RCA: 168] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. © RSNA, 2018.
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Affiliation(s)
- Katja Pinker
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Joanne Chin
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Amy N Melsaether
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Elizabeth A Morris
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
| | - Linda Moy
- From the Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P., J.C., E.A.M.); and Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York University of Medicine, 160 E 34th St, New York, NY 10016 (A.N.M., L.M.)
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Alves ITS, Condinho M, Custódio S, Pereira BF, Fernandes R, Gonçalves V, da Costa PJ, Lacerda R, Marques AR, Martins-Dias P, Nogueira GR, Neves AR, Pinho P, Rodrigues R, Rolo E, Silva J, Travessa A, Leite RP, Sousa A, Romão L. Genetics of personalized medicine: cancer and rare diseases. Cell Oncol (Dordr) 2018; 41:335-341. [PMID: 29633150 DOI: 10.1007/s13402-018-0379-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/21/2018] [Indexed: 12/28/2022] Open
Abstract
The 21st annual meeting of the Portuguese Society of Human Genetics (SPGH), organized by Luísa Romão, Ana Sousa and Rosário Pinto Leite, was held in Caparica, Portugal, from the 16th to the 18th of November 2017. Having entered an era in which personalized medicine is emerging as a paradigm for disease diagnosis, treatment and prevention, the program of this meeting intended to include lectures by leading national and international scientists presenting exceptional findings on the genetics of personalized medicine. Various topics were discussed, including cancer genetics, transcriptome dynamics and novel therapeutics for cancers and rare disorders that are designed to specifically target molecular alterations in individual patients. Several panel discussions were held to emphasize (ethical) issues associated with personalized medicine, including genetic cancer counseling.
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Affiliation(s)
- Inês Teles Siefers Alves
- Department of Cell Biology and Biochemistry, Springer Science + Business Media B.V, Van Godewijckstraat 30, 3311, GX, Dordrecht, The Netherlands.
| | - Manuel Condinho
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Sónia Custódio
- Medical Genetics Service, Pediatric Department, Hospital Santa Maria, Lisbon, Portugal
| | - Bruna F Pereira
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Rafael Fernandes
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Vânia Gonçalves
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Paulo J da Costa
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Rafaela Lacerda
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Ana Rita Marques
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Patrícia Martins-Dias
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Gonçalo R Nogueira
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Ana Rita Neves
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Patrícia Pinho
- Genetics Laboratory, Hospital Center of Trás-os-Montes and Alto Douro, Vila Real, Portugal
| | - Raquel Rodrigues
- Medical Genetics Service, Pediatric Department, Hospital Santa Maria, Lisbon, Portugal
| | - Eva Rolo
- Medical Genetics Service, Pediatric Department, Hospital Santa Maria, Lisbon, Portugal
| | - Joana Silva
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - André Travessa
- Medical Genetics Service, Pediatric Department, Hospital Santa Maria, Lisbon, Portugal
| | - Rosário Pinto Leite
- Genetics Laboratory, Hospital Center of Trás-os-Montes and Alto Douro, Vila Real, Portugal
| | - Ana Sousa
- Medical Genetics Service, Pediatric Department, Hospital Santa Maria, Lisbon, Portugal
| | - Luísa Romão
- Department of Human Genetics, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
- Biosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
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Fatai AA, Gamieldien J. A 35-gene signature discriminates between rapidly- and slowly-progressing glioblastoma multiforme and predicts survival in known subtypes of the cancer. BMC Cancer 2018; 18:377. [PMID: 29614978 PMCID: PMC5883543 DOI: 10.1186/s12885-018-4103-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 02/06/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Gene expression can be employed for the discovery of prognostic gene or multigene signatures cancer. In this study, we assessed the prognostic value of a 35-gene expression signature selected by pathway and machine learning based methods in adjuvant therapy-linked glioblastoma multiforme (GBM) patients from the Cancer Genome Atlas. METHODS Genes with high expression variance was subjected to pathway enrichment analysis and those having roles in chemoradioresistance pathways were used in expression-based feature selection. A modified Support Vector Machine Recursive Feature Elimination algorithm was employed to select a subset of these genes that discriminated between rapidly-progressing and slowly-progressing patients. RESULTS Survival analysis on TCGA samples not used in feature selection and samples from four GBM subclasses, as well as from an entirely independent study, showed that the 35-gene signature discriminated between the survival groups in all cases (p<0.05) and could accurately predict survival irrespective of the subtype. In a multivariate analysis, the signature predicted progression-free and overall survival independently of other factors considered. CONCLUSION We propose that the performance of the signature makes it an attractive candidate for further studies to assess its utility as a clinical prognostic and predictive biomarker in GBM patients. Additionally, the signature genes may also be useful therapeutic targets to improve both progression-free and overall survival in GBM patients.
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Affiliation(s)
- Azeez A Fatai
- South African Bioinformatics Institute and SAMRC Unit for Bioinformatics Capacity Development, University of the Western Cape, Bellville, 7535, Western Cape, 7530, South Africa
| | - Junaid Gamieldien
- South African Bioinformatics Institute and SAMRC Unit for Bioinformatics Capacity Development, University of the Western Cape, Bellville, 7535, Western Cape, 7530, South Africa.
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32
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He L, Tang J, Andersson EI, Timonen S, Koschmieder S, Wennerberg K, Mustjoki S, Aittokallio T. Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients. Cancer Res 2018; 78:2407-2418. [DOI: 10.1158/0008-5472.can-17-3644] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 01/17/2018] [Accepted: 02/20/2018] [Indexed: 11/16/2022]
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Litten RZ, Falk DE, Ryan ML, Fertig J, Leggio L. Advances in Pharmacotherapy Development: Human Clinical Studies. Handb Exp Pharmacol 2018; 248:579-613. [PMID: 29294197 DOI: 10.1007/164_2017_79] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
For more than 25 years, researchers have made advances in developing medications to treat alcohol use disorder (AUD), highlighted by the US Food and Drug Administration's (FDA's) approval of disulfiram, naltrexone (oral and long-acting), and acamprosate. These medications are also approved in Europe, where the European Medicines Agency (EMA) recently added a fourth medication, nalmefene, for AUD. Despite these advances, today's medications have a small effect size, showing efficacy for only a limited number of individuals with AUD. However, a host of new medications, which act on variety of pharmacologic targets, are in the pipeline and have been evaluated in numerous human studies. This article reviews the efficacy and safety of medications currently being tested in human trials and looks at ongoing efforts to identify candidate compounds in human studies. As mentioned in the National Institute on Alcohol Abuse and Alcoholism's Strategic Plan 2017-2021 ( https://www.niaaa.nih.gov/sites/default/files/StrategicPlan_NIAAA_optimized_2017-2020.pdf ), medications development remains a high priority. By developing more effective and safe medications, and identifying those patients who will benefit the most from these treatments, we can provide clinicians with the tools they need to treat this devastating disorder, providing relief for patients and their families and markedly improving public health and safety.
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Affiliation(s)
- Raye Z Litten
- Division of Medications Development, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA.
| | - Daniel E Falk
- Division of Medications Development, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Megan L Ryan
- Division of Medications Development, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Joanne Fertig
- Division of Medications Development, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - Lorenzo Leggio
- Section of Clinical Psychoneuroendocrinology and Neuropsychopharmacology, National Institute on Alcohol Abuse and Alcoholism and National Institute on Drug Abuse, Bethesda, MD, USA
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Ravikumar B, Aittokallio T. Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery. Expert Opin Drug Discov 2017; 13:179-192. [DOI: 10.1080/17460441.2018.1413089] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Balaguru Ravikumar
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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35
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Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology. Methods 2017; 129:60-80. [DOI: 10.1016/j.ymeth.2017.05.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Revised: 04/04/2017] [Accepted: 05/18/2017] [Indexed: 01/19/2023] Open
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Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid- and lymphoid-derived hematologic malignancies. Proc Natl Acad Sci U S A 2017; 114:E7554-E7563. [PMID: 28784769 DOI: 10.1073/pnas.1703094114] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Translating the genetic and epigenetic heterogeneity underlying human cancers into therapeutic strategies is an ongoing challenge. Large-scale sequencing efforts have uncovered a spectrum of mutations in many hematologic malignancies, including acute myeloid leukemia (AML), suggesting that combinations of agents will be required to treat these diseases effectively. Combinatorial approaches will also be critical for combating the emergence of genetically heterogeneous subclones, rescue signals in the microenvironment, and tumor-intrinsic feedback pathways that all contribute to disease relapse. To identify novel and effective drug combinations, we performed ex vivo sensitivity profiling of 122 primary patient samples from a variety of hematologic malignancies against a panel of 48 drug combinations. The combinations were designed as drug pairs that target nonoverlapping biological pathways and comprise drugs from different classes, preferably with Food and Drug Administration approval. A combination ratio (CR) was derived for each drug pair, and CRs were evaluated with respect to diagnostic categories as well as against genetic, cytogenetic, and cellular phenotypes of specimens from the two largest disease categories: AML and chronic lymphocytic leukemia (CLL). Nearly all tested combinations involving a BCL2 inhibitor showed additional benefit in patients with myeloid malignancies, whereas select combinations involving PI3K, CSF1R, or bromodomain inhibitors showed preferential benefit in lymphoid malignancies. Expanded analyses of patients with AML and CLL revealed specific patterns of ex vivo drug combination efficacy that were associated with select genetic, cytogenetic, and phenotypic disease subsets, warranting further evaluation. These findings highlight the heuristic value of an integrated functional genomic approach to the identification of novel treatment strategies for hematologic malignancies.
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Dermit M, Dokal A, Cutillas PR. Approaches to identify kinase dependencies in cancer signalling networks. FEBS Lett 2017; 591:2577-2592. [DOI: 10.1002/1873-3468.12748] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 06/27/2017] [Accepted: 07/03/2017] [Indexed: 12/18/2022]
Affiliation(s)
- Maria Dermit
- Cell Signalling & Proteomics Group; Barts Cancer Institute (CRUK Centre); Queen Mary University of London; UK
| | - Arran Dokal
- Cell Signalling & Proteomics Group; Barts Cancer Institute (CRUK Centre); Queen Mary University of London; UK
| | - Pedro R. Cutillas
- Cell Signalling & Proteomics Group; Barts Cancer Institute (CRUK Centre); Queen Mary University of London; UK
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Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer. Pharmacol Res 2017; 124:20-33. [PMID: 28735000 DOI: 10.1016/j.phrs.2017.07.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 06/09/2017] [Accepted: 07/14/2017] [Indexed: 12/12/2022]
Abstract
Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine.
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Wu Y, Zhang D, Wu B, Quan Y, Liu D, Li Y, Zhang X. Synergistic Activity of an Antimetabolite Drug and Tyrosine Kinase Inhibitors against Breast Cancer Cells. Chem Pharm Bull (Tokyo) 2017; 65:768-775. [PMID: 28539531 DOI: 10.1248/cpb.c17-00261] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Antimetabolite drugs, including the adenosine deaminase inhibitor cladribine, have been shown to induce apoptosis in a variety of cancer cells, and have been widely used in clinical trials of various cancers in conjunction with tyrosine kinase inhibitors (TKIs). Combination treatment with cladribine and gefitinib or dasatinib is expected to have a synergistic inhibitory effect on breast cancer cell growth. Our results demonstrated that the combination treatment had synergistic activity against human breast cancer (MCF-7) cells, enhanced G2/M cell arrest and reactive oxygen species (ROS) generation, and increased the loss of mitochondrial membrane potential and cell apoptosis. In addition, the combination treatment decreased Bcl-2 expression. Our results demonstrated that cladribine in combination with gefitinib or dasatinib exerted synergistic anticancer effects on MCF-7 cells by inducing cell cycle arrest, ROS production and apoptosis through the mitochondria-mediated intrinsic pathway.
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Affiliation(s)
- Yushan Wu
- School of Life Sciences, Shandong University of Technology
| | - Dongxing Zhang
- School of Life Sciences, Shandong University of Technology
| | - Baofan Wu
- Medical Department, Heze Municipal Hospital
| | - Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University
| | - Dongwu Liu
- School of Life Sciences, Shandong University of Technology
| | | | - Xiuzhen Zhang
- School of Life Sciences, Shandong University of Technology
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Beijersbergen RL, Wessels LF, Bernards R. Synthetic Lethality in Cancer Therapeutics. ANNUAL REVIEW OF CANCER BIOLOGY 2017. [DOI: 10.1146/annurev-cancerbio-042016-073434] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Treatment with targeted drugs has primarily focused on the genes and pathways that are mutated in cancer, which severely limits the repertoire of drug targets. Synthetic lethality exploits the notion that the presence of a mutation in a cancer gene is often associated with a new vulnerability that can be targeted therapeutically, thus greatly expanding the arsenal of potential drug targets. Here we discuss both the experimental and the computational biology tools that can be used to identify synthetic lethal interactions. We also discuss strategies for using synthetic lethality to discover new drug targets and in the rational design of more potent drug combinations. We review the progress made and future opportunities offered by synthetic lethal approaches to treating cancer more effectively.
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Affiliation(s)
- Roderick L. Beijersbergen
- Division of Molecular Carcinogenesis and Cancer Genomics Centre Netherlands, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Lodewyk F.A. Wessels
- Division of Molecular Carcinogenesis and Cancer Genomics Centre Netherlands, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - René Bernards
- Division of Molecular Carcinogenesis and Cancer Genomics Centre Netherlands, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
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41
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Tan AC, Vyse S, Huang PH. Exploiting receptor tyrosine kinase co-activation for cancer therapy. Drug Discov Today 2017; 22:72-84. [PMID: 27452454 PMCID: PMC5346155 DOI: 10.1016/j.drudis.2016.07.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Revised: 06/15/2016] [Accepted: 07/15/2016] [Indexed: 01/04/2023]
Abstract
Studies over the past decade have shown that many cancers have evolved receptor tyrosine kinase (RTK) co-activation as a mechanism to drive tumour progression and limit the lethal effects of therapy. This review summarises the general principles of RTK co-activation and discusses approaches to exploit this phenomenon in cancer therapy and drug discovery. Computational strategies to predict kinase co-dependencies by integrating drug screening data and kinase inhibitor selectivity profiles will also be described. We offer a perspective on the implications of RTK co-activation on tumour heterogeneity and cancer evolution and conclude by surveying emerging computational and experimental approaches that will provide insights into RTK co-activation biology and deliver new developments in effective cancer therapies.
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Affiliation(s)
- Aik-Choon Tan
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Simon Vyse
- Division of Cancer Biology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Paul H Huang
- Division of Cancer Biology, The Institute of Cancer Research, London SW3 6JB, UK.
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42
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Abstract
A long-standing paradigm in drug discovery has been the concept of designing maximally selective drugs to act on individual targets considered to underlie a disease of interest. Nonetheless, although some drugs have proven to be successful, many more potential drugs identified by the "one gene, one drug, one disease" approach have been found to be less effective than expected or to cause notable side effects. Advances in systems biology and high-throughput in-depth genomic profiling technologies along with an analysis of the successful and failed drugs uncovered that the prominent factor to determine drug sensitivity is the intrinsic robustness of the response of biological systems in the face of perturbations. The complexity of the molecular and cellular bases of systems responses to drug interventions has fostered an increased interest in systems-oriented approaches to drug discovery. Consonant with this knowledge of the multifactorial mechanistic basis of drug sensitivity and resistance is the application of network-based approaches for the identification of molecular (multi-)feature signatures associated with desired (multi-)drug phenotypic profiles. This chapter illustrates the principal network analysis and inference techniques which have found application in systems-oriented drug design and considers their benefits and drawbacks in relation to the nature of the data produced by network pharmacology.
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43
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Tang J. Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations. Methods Mol Biol 2017; 1636:485-506. [PMID: 28730498 DOI: 10.1007/978-1-4939-7154-1_30] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.
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Affiliation(s)
- Jing Tang
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, 00290, Helsinki, Finland. .,Department of Mathematics and Statistics, University of Turku, Turku, Finland.
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Hu XQ, Sun Y, Lau E, Zhao M, Su SB. Advances in Synergistic Combinations of Chinese Herbal Medicine for the Treatment of Cancer. Curr Cancer Drug Targets 2016; 16:346-56. [PMID: 26638885 PMCID: PMC5425653 DOI: 10.2174/1568009616666151207105851] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2015] [Revised: 09/15/2015] [Accepted: 12/04/2015] [Indexed: 12/13/2022]
Abstract
The complex pathology of cancer development requires correspondingly complex treatments. The traditional application of individual single-target drugs fails to sufficiently treat cancer with durable therapeutic effects and tolerable adverse events. Therefore, synergistic combinations of drugs represent a promising way to enhance efficacy, overcome toxicity and optimize safety. Chinese Herbal Medicines (CHMs) have long been used as such synergistic combinations. Therefore, we summarized the synergistic combinations of CHMs used in the treatment of cancer and their roles in chemotherapy in terms of enhancing efficacy, reducing side effects, immune modulation, as well as abrogating drug resistance. Our conclusions support the development of further science-based holistic modalities for cancer care.
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Affiliation(s)
| | | | | | | | - Shi-Bing Su
- Department of Research Center for Traditional Chinese Medicine Complexity System, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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45
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Weiss A, Nowak-Sliwinska P. Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders. SLAS Technol 2016; 22:254-275. [DOI: 10.1177/2472630316682338] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.
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Affiliation(s)
- Andrea Weiss
- Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
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46
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Liu H, Guo M, Xue T, Guan J, Luo L, Zhuang Z. Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks. BMC SYSTEMS BIOLOGY 2016; 10:131. [PMID: 28155715 PMCID: PMC5260106 DOI: 10.1186/s12918-016-0362-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background One of the most challenging tasks in the exploration of anti-aging is to discover drugs that can promote longevity and delay the incidence of age-associated diseases of human. Up to date, a number of drugs, including some antioxidants, metabolites and synthetic compounds, have been found to effectively delay the aging of nematodes and insects. Results We proposed a label propagation algorithm on drug-protein network to infer drugs that can extend the lifespan of C. elegans. We collected a set of drugs of which functions on lifespan extension of C. elegans have been reliably determined, and then built a large-scale drug-protein network by collecting a set of high-confidence drugprotein interactions. A label propagation algorithm was run on the drug-protein bipartite network to predict new drugs with lifespan-extending effect on C. elegans. We calibrated the performance of the proposed method by conducting performance comparison with two classical models, kNN and SVM. We also showed that the screened drugs significantly mediate in the aging-related pathways, and have higher chemical similarities to the effective drugs than ineffective drugs in promoting longevity of C. elegans. Moreover, we carried out wet-lab experiments to verify a screened drugs, 2- Bromo-4’-nitroacetophenone, and found that it can effectively extend the lifespan of C. elegans. These results showed that our method is effective in screening lifespanextending drugs in C. elegans. Conclusions In this paper, we proposed a semi-supervised algorithm to predict drugs with lifespan-extending effects on C. elegans. In silico empirical evaluations and in vivo experiments in C. elegans have demonstrated that our method can effectively narrow down the scope of candidate drugs needed to be verified by wet lab experiments. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0362-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hui Liu
- Changzhou University, Jiangsu, 213164, China.,Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China
| | | | - Ting Xue
- Changzhou University, Jiangsu, 213164, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China
| | - Libo Luo
- Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China. @fudan.edu.cn
| | - Ziheng Zhuang
- Changzhou University, Jiangsu, 213164, China. .,Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China.
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47
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Weiss A, Nowak-Sliwinska P. Current Trends in Multidrug Optimization. JOURNAL OF LABORATORY AUTOMATION 2016:2211068216682338. [PMID: 28095178 DOI: 10.1177/2211068216682338] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.
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Affiliation(s)
- Andrea Weiss
- 1 Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
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48
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Jaeger S, Igea A, Arroyo R, Alcalde V, Canovas B, Orozco M, Nebreda AR, Aloy P. Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer. Cancer Res 2016; 77:459-469. [DOI: 10.1158/0008-5472.can-16-0097] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 10/07/2016] [Accepted: 10/25/2016] [Indexed: 11/16/2022]
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49
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Molecular Changes During Acute Myeloid Leukemia (AML) Evolution and Identification of Novel Treatment Strategies Through Molecular Stratification. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2016; 144:383-436. [PMID: 27865463 DOI: 10.1016/bs.pmbts.2016.09.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by impaired differentiation and uncontrollable proliferation of myeloid progenitor cells. Due to high relapse rates, overall survival for this rapidly progressing disease is poor. The significant challenge in AML treatment is disease heterogeneity stemming from variability in maturation state of leukemic cells of origin, genetic aberrations among patients, and existence of multiple disease clones within a single patient. Disease heterogeneity and the lack of biomarkers for drug sensitivity lie at the root of treatment failure as well as selective efficacy of AML chemotherapies and the emergence of drug resistance. Furthermore, standard-of-care treatment is aggressive, presenting significant tolerability concerns to the commonly advanced-age AML patient. In this review, we examine the concept and potential of molecular stratification, particularly with biologically relevant drug responses, in identifying low-toxicity precision therapeutic combinations and clinically relevant biomarkers for AML patient care as a way to overcome these challenges in AML treatment.
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50
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Abstract
Background The advance in targeted therapy has greatly increased the effectiveness of clinical cancer therapy and reduced the cytotoxicity of treatments to normal cells. However, patients still suffer from cancer relapse due to the occurrence of drug resistance. It is of great need to explore potential combinatorial drug therapy since individual drug alone may not be sufficient to inhibit continuous activation of cancer-addicted genes or pathways. The DREAM challenge has confirmed the potentiality of computational methods for predicting synergistic drug combinations, while the prediction accuracy can be further improved. Methods Based on previous reports, we hypothesized the similarity in biological functions or genes perturbed by two drugs can determine their synergistic effects. To test the feasibility of the hypothesis, we proposed three scoring systems: co-gene score, co-GS score, and co-gene/GS score, measuring the similarities in genes with significant expressional changes, enriched gene sets, and significantly changed genes within an enriched gene sets between a pair of drugs, respectively. Performances of these scoring systems were evaluated by the probabilistic c-index (PC-index) devised by the DREAM consortium. We also applied the proposed method to the Connectivity Map dataset to explore more potential synergistic drug combinations. Results Using a gold standard derived by the DREAM consortium, we confirmed the prediction power of the three scoring systems (all P-values < 0.05). The co-gene/GS score achieved the best prediction of drug synergy (PC-index = 0.663, P-value < 0.0001), outperforming all methods proposed during DREAM challenge. Furthermore, a binary classification test showed that co-gene/GS scoring was highly accurate and specific. Since our method is constructed on a gene set-based analysis, in addition to synergy prediction, it provides insights into the functional relevance of drug combinations and the underlying mechanisms by which drugs achieve synergy. Conclusions Here we proposed a novel and simple method to predict and investigate drug synergy, and validated its efficacy to accurately predict synergistic drug combinations and to comprehensively explore their underlying mechanisms. The method is widely applicable to expression profiles of other drug treatments and is expected to accelerate the realization of precision cancer treatment. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0310-3) contains supplementary material, which is available to authorized users.
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