1
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Krushkal J, Zhao Y, Roney K, Zhu W, Brooks A, Wilsker D, Parchment RE, McShane LM, Doroshow JH. Association of changes in expression of HDAC and SIRT genes after drug treatment with cancer cell line sensitivity to kinase inhibitors. Epigenetics 2024; 19:2309824. [PMID: 38369747 PMCID: PMC10878021 DOI: 10.1080/15592294.2024.2309824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 01/14/2024] [Indexed: 02/20/2024] Open
Abstract
Histone deacetylases (HDACs) and sirtuins (SIRTs) are important epigenetic regulators of cancer pathways. There is a limited understanding of how transcriptional regulation of their genes is affected by chemotherapeutic agents, and how such transcriptional changes affect tumour sensitivity to drug treatment. We investigated the concerted transcriptional response of HDAC and SIRT genes to 15 approved antitumor agents in the NCI-60 cancer cell line panel. Antitumor agents with diverse mechanisms of action induced upregulation or downregulation of multiple HDAC and SIRT genes. HDAC5 was upregulated by dasatinib and erlotinib in the majority of the cell lines. Tumour cell line sensitivity to kinase inhibitors was associated with upregulation of HDAC5, HDAC1, and several SIRT genes. We confirmed changes in HDAC and SIRT expression in independent datasets. We also experimentally validated the upregulation of HDAC5 mRNA and protein expression by dasatinib in the highly sensitive IGROV1 cell line. HDAC5 was not upregulated in the UACC-257 cell line resistant to dasatinib. The effects of cancer drug treatment on expression of HDAC and SIRT genes may influence chemosensitivity and may need to be considered during chemotherapy.
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Affiliation(s)
- Julia Krushkal
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA
| | - Yingdong Zhao
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA
| | - Kyle Roney
- Department of Biostatistics and Bioinformatics, George Washington University, Washington, DC, USA
| | - Weimin Zhu
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Alan Brooks
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Deborah Wilsker
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Ralph E. Parchment
- Clinical Pharmacodynamic Biomarkers Program, Applied/Developmental Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Lisa M. McShane
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA
| | - James H. Doroshow
- Division of Cancer Treatment and Diagnosis and Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
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2
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Abbasi F, Rousu J. New methods for drug synergy prediction: A mini-review. Curr Opin Struct Biol 2024; 86:102827. [PMID: 38705070 DOI: 10.1016/j.sbi.2024.102827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 04/12/2024] [Accepted: 04/12/2024] [Indexed: 05/07/2024]
Abstract
In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these articles under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the articles deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.
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Affiliation(s)
- Fatemeh Abbasi
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Juho Rousu
- Department of Computer Science, Aalto University, Espoo, Finland.
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3
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Bashi AC, Coker EA, Bulusu KC, Jaaks P, Crafter C, Lightfoot H, Milo M, McCarten K, Jenkins DF, van der Meer D, Lynch JT, Barthorpe S, Andersen CL, Barry ST, Beck A, Cidado J, Gordon JA, Hall C, Hall J, Mali I, Mironenko T, Mongeon K, Morris J, Richardson L, Smith PD, Tavana O, Tolley C, Thomas F, Willis BS, Yang W, O'Connor MJ, McDermott U, Critchlow SE, Drew L, Fawell SE, Mettetal JT, Garnett MJ. Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations. Cancer Discov 2024; 14:846-865. [PMID: 38456804 PMCID: PMC11061612 DOI: 10.1158/2159-8290.cd-23-0388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 11/01/2023] [Accepted: 02/02/2024] [Indexed: 03/09/2024]
Abstract
Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific "emergent" biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets. SIGNIFICANCE We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.
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Affiliation(s)
| | | | | | | | | | | | - Marta Milo
- Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | | | | | | | | | - Syd Barthorpe
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | | | | | | | - Caitlin Hall
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | - James Hall
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | - Iman Mali
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | - James Morris
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | - Paul D. Smith
- Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | - Omid Tavana
- Oncology R&D, AstraZeneca, Waltham, Massachusetts
| | | | | | | | - Wanjuan Yang
- Wellcome Sanger Institute, Cambridge, United Kingdom
| | | | | | | | - Lisa Drew
- Oncology R&D, AstraZeneca, Waltham, Massachusetts
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4
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Zhao X, Xu J, Shui Y, Xu M, Hu J, Liu X, Che K, Wang J, Liu Y. PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction. J Cheminform 2024; 16:41. [PMID: 38622663 PMCID: PMC11017561 DOI: 10.1186/s13321-024-00839-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
MOTIVATION Drug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines. RESULTS In this paper, we proposed a Permutable feature fusion network for Drug-Drug Synergy prediction, named PermuteDDS. PermuteDDS takes multiple representations of drugs and cell lines as input and employs a permutable fusion mechanism to combine drug and cell line features. In experiments, PermuteDDS exhibits state-of-the-art performance on two benchmark data sets. Additionally, the results on independent test set grouped by different tissues reveal that PermuteDDS has good generalization performance. We believed that PermuteDDS is an effective and valuable tool for identifying synergistic drug combinations. It is publicly available at https://github.com/littlewei-lazy/PermuteDDS . SCIENTIFIC CONTRIBUTION First, this paper proposes a permutable feature fusion network for predicting drug synergy termed PermuteDDS, which extract diverse information from multiple drug representations and cell line representations. Second, the permutable fusion mechanism combine the drug and cell line features by integrating information of different channels, enabling the utilization of complex relationships between drugs and cell lines. Third, comparative and ablation experiments provide evidence of the efficacy of PermuteDDS in predicting drug-drug synergy.
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Affiliation(s)
- Xinwei Zhao
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Junqing Xu
- The Second Clinical Medical School, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Youyuan Shui
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Mengdie Xu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
| | - Jie Hu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China
| | - Xiaoyan Liu
- Faculty of Computing, Harbin Institute of Technology, No. 92 West Da Zhi St, Harbin, 150001, Heilongjiang, China
| | - Kai Che
- Xi'an Aeronautics Computing Technique Research Institute, AVIC, No. 156, TaiBai Nroth Road, Xi'an, 710068, Shanxi, China
- Aviation Key Laboratory of Science and Technology on Airborne and Missleborne Computer, Xi'an, 710065, Shanxi, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China.
| | - Yun Liu
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
- Institute of Medical Informatics and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 210029, Jiangsu, China.
- Department of Information, the First Affiliated Hospital, Nanjing Medical University, No. 300 Guang Zhou Road, Nanjing, 210029, Jiangsu, China.
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Huangfu X, Zhang C, Li H, Li S, Li Y. SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations. Comput Biol Chem 2024; 110:108054. [PMID: 38522389 DOI: 10.1016/j.compbiolchem.2024.108054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/22/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Network-based Synergy prediction (SNSynergy), for predicting synergistic effects towards new cell lines and new drug combinations by two locally weighted models CLSN and DCSN. This framework only requires a small amount of auxiliary data, like genomics information of cell lines and the molecular fingerprints or targets of drugs. Based on the assumption that similar cell lines and similar drug combinations have similar synergistic effects, CLSN and DCSN predict synergy scores through capturing individual synergy contributions of nearest cell line and drug combination neighbors, respectively. High correlations between predicted and measured synergy scores on two leading cancer cell line pharmacogenomic screening datasets (the O'Neil dataset and the NCI-ALMANAC dataset) demonstrate the effectiveness and robustness of SNSynergy. Many of the identified drug combinations are consistent with previous studies, or have been explored in clinical settings against the specific cancer type, showing that SNSynergy has the potential to supply cost-saving and effective high-throughput screening for prioritizing the most applicable cell lines and the most promising drug combinations.
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Affiliation(s)
- Xiaosheng Huangfu
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Chengwei Zhang
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Hualong Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China; School of mathematics and statistics, Guangdong University of Technology, Guangzhou 510520, China
| | - Sile Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Yushuang Li
- School of Science, Yanshan University, Qinhuangdao, Hebei 066004, China.
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6
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Yan S, Zheng D. A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer. Interdiscip Sci 2024; 16:218-230. [PMID: 38183569 DOI: 10.1007/s12539-023-00596-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/08/2024]
Abstract
The exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and chemical structure information for the prediction of synergistic effects in drug combinations. GTextSyn employs a sentence classification model within the domain of Natural Language Processing (NLP), wherein drugs and cell lines are regarded as entities possessing biochemical relevance. Meanwhile, combinations of drug pairs and cell lines are construed as sentences with biochemical relational significance. To assess the efficacy of GTextSyn, we conduct a comparative analysis with alternative deep learning approaches using a standard benchmark dataset. The results from a five-fold cross-validation demonstrate a 49.5% reduction in Mean Square Error (MSE) achieved by GTextSyn, surpassing the performance of the next best method in the regression task. Furthermore, we conduct a comprehensive literature survey on the predicted novel drug combinations and find substantial support from prior experimental studies for many of the combinations identified by GTextSyn.
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Affiliation(s)
- Shiyu Yan
- School of Computer, University of South China, West Changsheng Road, Hengyang, 421001, Hunan, China.
| | - Ding Zheng
- School of Computer, University of South China, West Changsheng Road, Hengyang, 421001, Hunan, China
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7
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Mason-Osann E, Pomeroy AE, Palmer AC, Mettetal JT. Synergistic Drug Combinations Promote the Development of Resistance in Acute Myeloid Leukemia. Blood Cancer Discov 2024; 5:95-105. [PMID: 38232314 PMCID: PMC10905516 DOI: 10.1158/2643-3230.bcd-23-0067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/30/2023] [Accepted: 01/16/2024] [Indexed: 01/19/2024] Open
Abstract
Combination therapy is an important part of cancer treatment and is often employed to overcome or prevent drug resistance. Preclinical screening strategies often prioritize synergistic drug combinations; however, studies of antibiotic combinations show that synergistic drug interactions can accelerate the emergence of resistance because resistance to one drug depletes the effect of both. In this study, we aimed to determine whether synergy drives the development of resistance in cancer cell lines using live-cell imaging. Consistent with prior models of tumor evolution, we found that when controlling for activity, drug synergy is associated with increased probability of developing drug resistance. We demonstrate that these observations are an expected consequence of synergy: the fitness benefit of resisting a drug in a combination is greater in synergistic combinations than in nonsynergistic combinations. These data have important implications for preclinical strategies aiming to develop novel combinations of cancer therapies with robust and durable efficacy. SIGNIFICANCE Preclinical strategies to identify combinations for cancer treatment often focus on identifying synergistic combinations. This study shows that in AML cells combinations that rely on synergy can increase the likelihood of developing resistance, suggesting that combination screening strategies may benefit from a more holistic approach rather than focusing on drug synergy. See related commentary by Bhola and Letai, p. 81. This article is featured in Selected Articles from This Issue, p. 80.
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Affiliation(s)
| | - Amy E. Pomeroy
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Adam C. Palmer
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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8
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Ali KA, Shah RD, Dhar A, Myers NM, Nguyen C, Paul A, Mancuso JE, Scott Patterson A, Brody JP, Heiser D. Ex vivo discovery of synergistic drug combinations for hematologic malignancies. SLAS Discov 2024; 29:100129. [PMID: 38101570 DOI: 10.1016/j.slasd.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/13/2023] [Accepted: 12/09/2023] [Indexed: 12/17/2023]
Abstract
Combination therapies have improved outcomes for patients with acute myeloid leukemia (AML). However, these patients still have poor overall survival. Although many combination therapies are identified with high-throughput screening (HTS), these approaches are constrained to disease models that can be grown in large volumes (e.g., immortalized cell lines), which have limited translational utility. To identify more effective and personalized treatments, we need better strategies for screening and exploring potential combination therapies. Our objective was to develop an HTS platform for identifying effective combination therapies with highly translatable ex vivo disease models that use size-limited, primary samples from patients with leukemia (AML and myelodysplastic syndrome). We developed a system, ComboFlow, that comprises three main components: MiniFlow, ComboPooler, and AutoGater. MiniFlow conducts ex vivo drug screening with a miniaturized flow-cytometry assay that uses minimal amounts of patient sample to maximize throughput. ComboPooler incorporates computational methods to design efficient screens of pooled drug combinations. AutoGater is an automated gating classifier for flow cytometry that uses machine learning to rapidly analyze the large datasets generated by the assay. We used ComboFlow to efficiently screen more than 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. In this screen, ComboFlow identified the known synergistic combination of bortezomib and panobinostat. ComboFlow also identified a novel drug combination, dactinomycin and fludarabine, that synergistically killed leukemic cells in 35 % of AML samples. This combination also had limited effects in normal, hematopoietic progenitors. In conclusion, ComboFlow enables exploration of massive landscapes of drug combinations that were previously inaccessible in ex vivo models. We envision that ComboFlow can be used to discover more effective and personalized combination therapies for cancers amenable to ex vivo models.
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Affiliation(s)
- Kamran A Ali
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA; Department of Biomedical Engineering, University of California, Irvine, 3120 Natural Sciences II, Irvine, CA, 92697, USA.
| | - Reecha D Shah
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
| | - Anukriti Dhar
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
| | - Nina M Myers
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
| | | | - Arisa Paul
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
| | | | | | - James P Brody
- Department of Biomedical Engineering, University of California, Irvine, 3120 Natural Sciences II, Irvine, CA, 92697, USA
| | - Diane Heiser
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
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9
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Abd El-Hafeez T, Shams MY, Elshaier YAMM, Farghaly HM, Hassanien AE. Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs. Sci Rep 2024; 14:2428. [PMID: 38287066 PMCID: PMC10825182 DOI: 10.1038/s41598-024-52814-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/24/2024] [Indexed: 01/31/2024] Open
Abstract
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.
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Affiliation(s)
- Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt.
- Computer Science Unit, Deraya University, El-Minia, Egypt.
| | - Mahmoud Y Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Yaseen A M M Elshaier
- Department of Organic and Medicinal Chemistry, Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufia, Egypt
| | - Heba Mamdouh Farghaly
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt.
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10
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Guo Y, Hu H, Chen W, Yin H, Wu J, Hsieh CY, He Q, Cao J. SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction. Brief Bioinform 2024; 25:bbae015. [PMID: 38340091 PMCID: PMC10858681 DOI: 10.1093/bib/bbae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/18/2023] [Indexed: 02/12/2024] Open
Abstract
Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.
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Affiliation(s)
- Yue Guo
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
| | - Haitao Hu
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Polytechnic Institute, Zhejiang University, 269 Shixiang Road,310000, Hangzhou, Zhejiang, China
| | - Wenbo Chen
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Polytechnic Institute, Zhejiang University, 269 Shixiang Road,310000, Hangzhou, Zhejiang, China
| | - Hao Yin
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Polytechnic Institute, Zhejiang University, 269 Shixiang Road,310000, Hangzhou, Zhejiang, China
| | - Jian Wu
- Second Affiliated Hospital School of Medicine, School of Public Health, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
| | - Chang-Yu Hsieh
- The Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, 291 Fucheng Road, 310018, Hangzhou, Zhejiang, China
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
| | - Qiaojun He
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education, 310020, Hangzhou, Zhejiang, China
- The Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, 291 Fucheng Road, 310018, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
| | - Ji Cao
- Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
- Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education, 310020, Hangzhou, Zhejiang, China
- The Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, 291 Fucheng Road, 310018, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang, China
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11
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Liu M, Srivastava G, Ramanujam J, Brylinski M. Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects. Sci Rep 2024; 14:1668. [PMID: 38238448 PMCID: PMC10796434 DOI: 10.1038/s41598-024-51940-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 01/11/2024] [Indexed: 01/22/2024] Open
Abstract
Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that ML models trained on the augmented data consistently achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.
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Affiliation(s)
- Mengmeng Liu
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Gopal Srivastava
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - J Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA.
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA.
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12
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Zhang H, Kreis J, Schelhorn SE, Dahmen H, Grombacher T, Zühlsdorf M, Zenke FT, Guan Y. Mapping combinatorial drug effects to DNA damage response kinase inhibitors. Nat Commun 2023; 14:8310. [PMID: 38097586 PMCID: PMC10721915 DOI: 10.1038/s41467-023-44108-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
One fundamental principle that underlies various cancer treatments, such as traditional chemotherapy and radiotherapy, involves the induction of catastrophic DNA damage, leading to the apoptosis of cancer cells. In our study, we conduct a comprehensive dose-response combination screening focused on inhibitors that target key kinases involved in the DNA damage response (DDR): ATR, ATM, and DNA-PK. This screening involves 87 anti-cancer agents, including six DDR inhibitors, and encompasses 62 different cell lines spanning 12 types of tumors, resulting in a total of 17,912 combination treatment experiments. Within these combinations, we analyze the most effective and synergistic drug pairs across all tested cell lines, considering the variations among cancers originating from different tissues. Our analysis reveals inhibitors of five DDR-related pathways (DNA topoisomerase, PLK1 kinase, p53-inducible ribonucleotide reductase, PARP, and cell cycle checkpoint proteins) that exhibit strong combinatorial efficacy and synergy when used alongside ATM/ATR/DNA-PK inhibitors.
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Affiliation(s)
- Hanrui Zhang
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | | | | | | | | | | | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
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13
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Singh N, Khan FM, Bala L, Vera J, Wolkenhauer O, Pützer B, Logotheti S, Gupta SK. Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression. BMC Chem 2023; 17:161. [PMID: 37993971 PMCID: PMC10666365 DOI: 10.1186/s13065-023-01082-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023] Open
Abstract
Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.
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Affiliation(s)
- Nivedita Singh
- Department of Biochemistry, BBDCODS, BBD University, Lucknow, Uttar Pradesh, India
- MRC Laboratory for Molecular Cell Biology, University College London, London, UK
| | - Faiz M Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Lakshmi Bala
- Department of Biochemistry, BBDCODS, BBD University, Lucknow, Uttar Pradesh, India
| | - Julio Vera
- Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Erlangen, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Leibniz Institute for Food Systems Biology, Technical University of Munich, Munich, Germany
- Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India
- Stellenbosch Institute of Advanced Study, Wallenberg Research Centre, Stellenbosch University, Stellenbosch, South Africa
| | - Brigitte Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
| | - Stella Logotheti
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, Rostock, Germany
- DNA Damage Laboratory, Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou, Athens, Greece
| | - Shailendra K Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
- Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India.
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14
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Wang W, Yuan G, Wan S, Zheng Z, Liu D, Zhang H, Li J, Zhou Y, Wang X. A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs. Brief Bioinform 2023; 25:bbad522. [PMID: 38243692 PMCID: PMC10796255 DOI: 10.1093/bib/bbad522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 11/08/2023] [Accepted: 12/19/2023] [Indexed: 01/21/2024] Open
Abstract
Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.
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Affiliation(s)
- Wei Wang
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Gaolin Yuan
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Shitong Wan
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Ziwei Zheng
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
| | - Dong Liu
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Hongjun Zhang
- Hebi Instiute of Engineering and Technology, Henan Polytechnic University, 458030, China
| | - Juntao Li
- School of Mathematics and Information Science, Henan Normal University, 453007 Xinxiang, China
| | - Yun Zhou
- College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province 453007, China
| | - Xianfang Wang
- College of Computer Science and Technology Engineering, Henan Institute of Technology, 453000, China
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15
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Lee HM, Wright WC, Pan M, Low J, Currier D, Fang J, Singh S, Nance S, Delahunty I, Kim Y, Chapple RH, Zhang Y, Liu X, Steele JA, Qi J, Pruett-Miller SM, Easton J, Chen T, Yang J, Durbin AD, Geeleher P. A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics. Nat Commun 2023; 14:7332. [PMID: 37957169 PMCID: PMC10643606 DOI: 10.1038/s41467-023-43134-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Combination chemotherapy is crucial for successfully treating cancer. However, the enormous number of possible drug combinations means discovering safe and effective combinations remains a significant challenge. To improve this process, we conduct large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritize neuroblastoma, the most common extracranial pediatric solid tumor, where ~50% of high-risk patients do not survive. Our screen examines all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest existing drug combination screens. Using dense drug-drug rescreening, we find that the top CRISPR-nominated drug combinations are more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discover that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using patient-derived xenograft (PDX) models. Our findings provide a valuable resource and demonstrate the feasibility of using targeted CRISPR knockout to discover combinations with common chemotherapeutics, a methodology with application across all cancers.
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Affiliation(s)
- Hyeong-Min Lee
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - William C Wright
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Min Pan
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jonathan Low
- Department of Chemical Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Duane Currier
- Department of Chemical Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jie Fang
- Department of Surgery, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Shivendra Singh
- Department of Surgery, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Stephanie Nance
- Division of Molecular Oncology, Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Ian Delahunty
- Division of Molecular Oncology, Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yuna Kim
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Richard H Chapple
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Yinwen Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Xueying Liu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jacob A Steele
- Center for Advanced Genome Engineering, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Cell and Molecular Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jun Qi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Shondra M Pruett-Miller
- Center for Advanced Genome Engineering, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Cell and Molecular Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - John Easton
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Taosheng Chen
- Department of Chemical Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Jun Yang
- Department of Surgery, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
- Department of Pathology and Laboratory Medicine, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Adam D Durbin
- Division of Molecular Oncology, Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
| | - Paul Geeleher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA.
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16
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Yan K, Jia R, Guo S. SynAI: an AI-driven cancer drugs synergism prediction platform. Bioinform Adv 2023; 3:vbad160. [PMID: 38023331 PMCID: PMC10660295 DOI: 10.1093/bioadv/vbad160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/13/2023] [Accepted: 11/08/2023] [Indexed: 12/01/2023]
Abstract
Summary The SynAI solution is a flexible AI-driven drug synergism prediction solution aiming to discover potential therapeutic value of compounds in early stage. Rather than providing a finite choice of drug combination or cell lines, SynAI is capable of predicting potential drug synergism/antagonism using in silico compound SMILE (Simplified Molecular Input Line Entry System) sequences. The AI core of SynAI platform has been trained against cell lines and compound pairs listed by NCI (National Cancer Institute)-Almanac and DurgCombDB datasets. In total, the training data consists of over 1 200 000 in vitro synergism tests on 150 cancer cell lines of different organ origins. Each cell line is tested against over 6000 pairs of FDA (Food and Drug Administration) approved compound combinations. Given one or both candidate compound in SMILE sequence, SynAI is able to predict the potential Bliss score of the combined compound test with the designated cell line without the needs of compound synthetization or structural analysis; thus can significantly reduce the candidate screening costs during the compound development. SynAI platform demonstrates a comparable performance to existing methods but offers more flexibilities for data input. Availability and implementation The evaluation version of SynAI is freely accessible online at https://synai.crownbio.com.
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Affiliation(s)
- Kuan Yan
- Data Science and Bioinformatics, Crown Bioscience, Suzhou, Jiangsu Province 215000, P.R. China
| | - Runjun Jia
- Data Science and Bioinformatics, Crown Bioscience, Suzhou, Jiangsu Province 215000, P.R. China
| | - Sheng Guo
- Data Science and Bioinformatics, Crown Bioscience, Suzhou, Jiangsu Province 215000, P.R. China
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17
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Morris J, Kunkel MW, White SL, Wishka DG, Lopez OD, Bowles L, Sellers Brady P, Ramsey P, Grams J, Rohrer T, Martin K, Dexheimer TS, Coussens NP, Evans D, Risbood P, Sonkin D, Williams JD, Polley EC, Collins JM, Doroshow JH, Teicher BA. Targeted Investigational Oncology Agents in the NCI-60: A Phenotypic Systems-based Resource. Mol Cancer Ther 2023; 22:1270-1279. [PMID: 37550087 PMCID: PMC10618733 DOI: 10.1158/1535-7163.mct-23-0267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/11/2023] [Accepted: 08/02/2023] [Indexed: 08/09/2023]
Abstract
The NCI-60 human tumor cell line panel has proved to be a useful tool for the global cancer research community in the search for novel chemotherapeutics. The publicly available cell line characterization and compound screening data from the NCI-60 assay have significantly contributed to the understanding of cellular mechanisms targeted by new oncology agents. Signature sensitivity/resistance patterns generated for a given chemotherapeutic agent against the NCI-60 panel have long served as fingerprint presentations that encompass target information and the mechanism of action associated with the tested agent. We report the establishment of a new public NCI-60 resource based on the cell line screening of a large and growing set of 175 FDA-approved oncology drugs (AOD) plus >825 clinical and investigational oncology agents (IOA), representing a diverse set (>250) of therapeutic targets and mechanisms. This data resource is available to the public (https://ioa.cancer.gov) and includes the raw data from the screening of the IOA and AOD collection along with an extensive set of visualization and analysis tools to allow for comparative study of individual test compounds and multiple compound sets.
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Affiliation(s)
- Joel Morris
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - Mark W. Kunkel
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - Stephen L. White
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - Donn G. Wishka
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - Omar D. Lopez
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - Lori Bowles
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - Penny Sellers Brady
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - Patricia Ramsey
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - Julie Grams
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - Tiffany Rohrer
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - Karen Martin
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - Thomas S. Dexheimer
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - Nathan P. Coussens
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - David Evans
- Target Validation and Screening Laboratory, Applied and Developmental Research Directorate, Frederick National, Laboratory for Cancer Research, Frederick, Maryland
| | - Prabhakar Risbood
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - Dmitriy Sonkin
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - John D. Williams
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - Eric C. Polley
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - Jerry M. Collins
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
| | - James H. Doroshow
- Division of Cancer Treatment and Diagnosis, NCI, Rockville, Maryland
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18
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Becker PS. Potent Personalized Venetoclax Partners for Acute Myeloid Leukemia Identified by Ex Vivo Drug Screening. Blood Cancer Discov 2023; 4:437-439. [PMID: 37824763 PMCID: PMC10625347 DOI: 10.1158/2643-3230.bcd-23-0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023] Open
Abstract
SUMMARY High-throughput screens (HTS) have been utilized to assess the efficacy of single drugs against patient tumor samples with the purpose of optimizing precision therapy, but testing the synergy of drug combinations can identify the ideal second drug to add. With novel sophisticated HTS, effective venetoclax combinations can be revealed that provide the cell state, phenotype, and molecular features of the susceptible and resistant cell populations. See related article by Eide, Kurtz et al., p. 452 (14) .
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Affiliation(s)
- Pamela S. Becker
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, California
- Department of Hematologic Malignancies Translational Science, City of Hope Beckman Research Institute, Duarte, California
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19
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Liu M, Srivastava G, Ramanujam J, Brylinski M. Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects. Res Sq 2023:rs.3.rs-3481858. [PMID: 37961281 PMCID: PMC10635365 DOI: 10.21203/rs.3.rs-3481858/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8,798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that Random Forest and Gradient Boosting Trees models trained on the augmented data achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.
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20
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Bertin P, Rector-Brooks J, Sharma D, Gaudelet T, Anighoro A, Gross T, Martínez-Peña F, Tang EL, Suraj MS, Regep C, Hayter JBR, Korablyov M, Valiante N, van der Sloot A, Tyers M, Roberts CES, Bronstein MM, Lairson LL, Taylor-King JP, Bengio Y. RECOVER identifies synergistic drug combinations in vitro through sequential model optimization. Cell Rep Methods 2023; 3:100599. [PMID: 37797618 PMCID: PMC10626197 DOI: 10.1016/j.crmeth.2023.100599] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 10/07/2023]
Abstract
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased toward synergistic agents and results do not generalize out of distribution. During 5 rounds of experimentation, we employ sequential model optimization with a deep learning model to select drug combinations increasingly enriched for synergism and active against a cancer cell line-evaluating only ∼5% of the total search space. Moreover, we find that learned drug embeddings (using structural information) begin to reflect biological mechanisms. In silico benchmarking suggests search queries are ∼5-10× enriched for highly synergistic drug combinations by using sequential rounds of evaluation when compared with random selection or ∼3× when using a pretrained model.
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Affiliation(s)
- Paul Bertin
- Mila, the Quebec AI Institute, Montreal, QC, Canada
| | | | | | | | | | | | | | - Eileen L Tang
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
| | | | | | | | | | | | - Almer van der Sloot
- IRIC, Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada
| | - Mike Tyers
- Program in Molecular Medicine, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada
| | | | - Michael M Bronstein
- Relation Therapeutics, London, UK; Department of Computer Science, University of Oxford, Oxford, UK
| | - Luke L Lairson
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
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21
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Mao B, Guo S. Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models. Cancer Res Commun 2023; 3:2146-2157. [PMID: 37830749 PMCID: PMC10591909 DOI: 10.1158/2767-9764.crc-23-0243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/31/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023]
Abstract
Drug combination therapy is a promising strategy for treating cancer; however, its efficacy and synergy require rigorous evaluation in preclinical studies before going to clinical trials. Existing methods have limited power to detect synergy in animal studies. Here, we introduce a novel approach to assess in vivo drug synergy with high sensitivity and low false discovery rate. It can accurately estimate combination index and synergy score under the Bliss independence model and the highest single agent (HSA) model without any assumption on tumor growth kinetics, study duration, data completeness and balance for tumor volume measurement. We show that our method can effectively validate in vitro drug synergy discovered from cell line assays in in vivo xenograft experiments, and can help to elucidate the mechanism of action for immune checkpoint inhibitors in syngeneic mouse models by combining an anti-PD-1 antibody and several tumor-infiltrating leukocytes depletion treatments. We provide a unified view of in vitro and in vivo synergy by presenting a parallelism between the fixed-dose in vitro and the 4-group in vivo combination studies, so they can be better designed, analyzed, and compared. We emphasize that combination index, when defined here via relative survival of tumor cells, is both dose and time dependent, and give guidelines on designing informative in vivo combination studies. We explain how to interpret and apply Bliss and HSA synergies. Finally, we provide an open-source software package named invivoSyn that enables automated analysis of in vivo synergy using our method and several other existing methods. SIGNIFICANCE This work presents a general solution to reliably determine in vivo drug synergy in single-dose 4-group animal combination studies.
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Affiliation(s)
- Binchen Mao
- Crown Bioscience Inc., Suzhou, Jiangsu, P.R. China
| | - Sheng Guo
- Crown Bioscience Inc., Suzhou, Jiangsu, P.R. China
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22
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Sun P, Cao Y, Qiu J, Kong J, Zhang S, Cao X. Inhibitory Mechanisms of Lekethromycin in Dog Liver Cytochrome P450 Enzymes Based on UPLC-MS/MS Cocktail Method. Molecules 2023; 28:7193. [PMID: 37894672 PMCID: PMC10609143 DOI: 10.3390/molecules28207193] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
Lekethromycin (LKMS) is a synthetic macrolide compound derivative intended for use as a veterinary medicine. Since there have been no in vitro studies evaluating its potential for drug-drug interactions related to cytochrome P450 (CYP450) enzymes, the effect of the inhibitory mechanisms of LKMS on CYP450 enzymes is still unclear. Thus, this study aimed to evaluate the inhibitory effects of LKMS on dog CYP450 enzymes. A cocktail approach using ultra-performance liquid chromatography-tandem mass spectrometry was conducted to investigate the inhibitory effect of LKMS on canine CYP450 enzymes. Typical probe substrates of phenacetin, coumarin, bupropion, tolbutamide, dextromethorphan, chlorzoxazone, and testosterone were used for CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2D6, CYP2E1, and CYP3A4, respectively. This study showed that LKMS might not be a time-dependent inhibitor. LKMS inhibited CYP2A6, CYP2B6, and CYP2D6 via mixed inhibition. LKMS exhibited mixed-type inhibition against the activity of CYP2A6 with an inhibition constant (Ki) value of 135.6 μΜ. LKMS inhibited CYP2B6 in a mixed way, with Ki values of 59.44 μM. A phenotyping study based on an inhibition assay indicated that CYP2D6 contributes to the biotransformation of LKMS. A mixed inhibition of CYP2D6 with Ki values of 64.87 μM was also observed. Given that this study was performed in vitro, further in vivo studies should be conducted to identify the interaction between LKMS and canine CYP450 enzymes to provide data support for the clinical application of LKMS and the avoidance of adverse interactions between other drugs.
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Affiliation(s)
- Pan Sun
- Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China; (P.S.); (Y.C.); (J.Q.); (J.K.); (S.Z.)
- Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
- Key Laboratory of Detection for Veterinary Drug Residues and Illegal Additives, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
| | - Yuying Cao
- Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China; (P.S.); (Y.C.); (J.Q.); (J.K.); (S.Z.)
- Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
- Key Laboratory of Detection for Veterinary Drug Residues and Illegal Additives, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
| | - Jicheng Qiu
- Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China; (P.S.); (Y.C.); (J.Q.); (J.K.); (S.Z.)
- Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
- Key Laboratory of Detection for Veterinary Drug Residues and Illegal Additives, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
| | - Jingyuan Kong
- Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China; (P.S.); (Y.C.); (J.Q.); (J.K.); (S.Z.)
- Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
- Key Laboratory of Detection for Veterinary Drug Residues and Illegal Additives, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
| | - Suxia Zhang
- Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China; (P.S.); (Y.C.); (J.Q.); (J.K.); (S.Z.)
- Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
- Key Laboratory of Detection for Veterinary Drug Residues and Illegal Additives, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
| | - Xingyuan Cao
- Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China; (P.S.); (Y.C.); (J.Q.); (J.K.); (S.Z.)
- Laboratory of Quality & Safety Risk Assessment for Animal Products on Chemical Hazards (Beijing), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
- Key Laboratory of Detection for Veterinary Drug Residues and Illegal Additives, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing 100193, China
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23
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Gauthier-Coles G, Rahimi F, Bröer A, Bröer S. Inhibition of GCN2 Reveals Synergy with Cell-Cycle Regulation and Proteostasis. Metabolites 2023; 13:1064. [PMID: 37887389 PMCID: PMC10609202 DOI: 10.3390/metabo13101064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/19/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023] Open
Abstract
The integrated stress response is a signaling network comprising four branches, each sensing different cellular stressors, converging on the phosphorylation of eIF2α to downregulate global translation and initiate recovery. One of these branches includes GCN2, which senses cellular amino acid insufficiency and participates in maintaining amino acid homeostasis. Previous studies have shown that GCN2 is a viable cancer target when amino acid stress is induced by inhibiting an additional target. In this light, we screened numerous drugs for their potential to synergize with the GCN2 inhibitor TAP20. The drug sensitivity of six cancer cell lines to a panel of 25 compounds was assessed. Each compound was then combined with TAP20 at concentrations below their IC50, and the impact on cell growth was evaluated. The strongly synergistic combinations were further characterized using synergy analyses and matrix-dependent invasion assays. Inhibitors of proteostasis and the MEK-ERK pathway, as well as the pan-CDK inhibitors, flavopiridol, and seliciclib, were potently synergistic with TAP20 in two cell lines. Among their common CDK targets was CDK7, which was more selectively targeted by THZ-1 and synergized with TAP20. Moreover, these combinations were partially synergistic when assessed using matrix-dependent invasion assays. However, TAP20 alone was sufficient to restrict invasion at concentrations well below its growth-inhibitory IC50. We conclude that GCN2 inhibition can be further explored in vivo as a cancer target.
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Affiliation(s)
- Gregory Gauthier-Coles
- Research School of Biology, Australian National University, Canberra, ACT 2601, Australia; (G.G.-C.); (F.R.); (A.B.)
- School of Medicine, Yale University, New Haven, CT 06504, USA
| | - Farid Rahimi
- Research School of Biology, Australian National University, Canberra, ACT 2601, Australia; (G.G.-C.); (F.R.); (A.B.)
| | - Angelika Bröer
- Research School of Biology, Australian National University, Canberra, ACT 2601, Australia; (G.G.-C.); (F.R.); (A.B.)
| | - Stefan Bröer
- Research School of Biology, Australian National University, Canberra, ACT 2601, Australia; (G.G.-C.); (F.R.); (A.B.)
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24
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Gu J, Bang D, Yi J, Lee S, Kim DK, Kim S. A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning. Brief Bioinform 2023; 24:bbad285. [PMID: 37544660 DOI: 10.1093/bib/bbad285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/05/2023] [Accepted: 07/21/2023] [Indexed: 08/08/2023] Open
Abstract
Combination therapies have brought significant advancements to the treatment of various diseases in the medical field. However, searching for effective drug combinations remains a major challenge due to the vast number of possible combinations. Biomedical knowledge graph (KG)-based methods have shown potential in predicting effective combinations for wide spectrum of diseases, but the lack of credible negative samples has limited the prediction performance of machine learning models. To address this issue, we propose a novel model-agnostic framework that leverages existing drug-drug interaction (DDI) data as a reliable negative dataset and employs supervised contrastive learning (SCL) to transform drug embedding vectors to be more suitable for drug combination prediction. We conducted extensive experiments using various network embedding algorithms, including random walk and graph neural networks, on a biomedical KG. Our framework significantly improved performance metrics compared to the baseline framework. We also provide embedding space visualizations and case studies that demonstrate the effectiveness of our approach. This work highlights the potential of using DDI data and SCL in finding tighter decision boundaries for predicting effective drug combinations.
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Affiliation(s)
- Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- AIGENDRUG Co., Ltd., 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Sangseon Lee
- Institute of Computer Technology Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
| | - Dong Kyu Kim
- PHARMGENSCIENCE Co., Ltd., 216, Dongjak-daero, 06554 Seoul, Republic of Korea
| | - Sun Kim
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Department of Computer Science and Engineering, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- AIGENDRUG Co., Ltd., 1, Gwanak-ro, 08826 Seoul, Republic of Korea
- Institute of Computer Technology, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea
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25
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Pan Y, Ren H, Lan L, Li Y, Huang T. Review of Predicting Synergistic Drug Combinations. Life (Basel) 2023; 13:1878. [PMID: 37763281 PMCID: PMC10533134 DOI: 10.3390/life13091878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 08/31/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.
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Affiliation(s)
- Yichen Pan
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
| | - Haotian Ren
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
| | - Liang Lan
- Department of Interactive Media, Hong Kong Baptist University, Hong Kong, China;
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Guangzhou Laboratory, Guangzhou 510005, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (Y.P.); (H.R.)
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26
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Dong Z, Zhang H, Chen Y, Payne PRO, Li F. Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling. Cancers (Basel) 2023; 15:4210. [PMID: 37686486 PMCID: PMC10486573 DOI: 10.3390/cancers15174210] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.
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Affiliation(s)
- Zehao Dong
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; (Z.D.); (Y.C.)
| | - Heming Zhang
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
| | - Yixin Chen
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; (Z.D.); (Y.C.)
| | - Philip R. O. Payne
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
| | - Fuhai Li
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
- Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA
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27
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More P, Ngaffo JAM, Goedtel-Armbrust U, Hähnel PS, Hartwig UF, Kindler T, Wojnowski L. Transcriptional Response to Standard AML Drugs Identifies Synergistic Combinations. Int J Mol Sci 2023; 24:12926. [PMID: 37629110 PMCID: PMC10455220 DOI: 10.3390/ijms241612926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Unlike genomic alterations, gene expression profiles have not been widely used to refine cancer therapies. We analyzed transcriptional changes in acute myeloid leukemia (AML) cell lines in response to standard first-line AML drugs cytarabine and daunorubicin by means of RNA sequencing. Those changes were highly cell- and treatment-specific. By comparing the changes unique to treatment-sensitive and treatment-resistant AML cells, we enriched for treatment-relevant genes. Those genes were associated with drug response-specific pathways, including calcium ion-dependent exocytosis and chromatin remodeling. Pharmacological mimicking of those changes using EGFR and MEK inhibitors enhanced the response to daunorubicin with minimum standalone cytotoxicity. The synergistic response was observed even in the cell lines beyond those used for the discovery, including a primary AML sample. Additionally, publicly available cytotoxicity data confirmed the synergistic effect of EGFR inhibitors in combination with daunorubicin in all 60 investigated cancer cell lines. In conclusion, we demonstrate the utility of treatment-evoked gene expression changes to formulate rational drug combinations. This approach could improve the standard AML therapy, especially in older patients.
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Affiliation(s)
- Piyush More
- Department of Pharmacology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany; (J.A.M.N.); (U.G.-A.); (L.W.)
| | - Joëlle Aurelie Mekontso Ngaffo
- Department of Pharmacology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany; (J.A.M.N.); (U.G.-A.); (L.W.)
- Leibniz Institute for New Materials, 66123 Saarbrücken, Germany
| | - Ute Goedtel-Armbrust
- Department of Pharmacology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany; (J.A.M.N.); (U.G.-A.); (L.W.)
| | - Patricia S. Hähnel
- University Cancer Center (UCT) Mainz, Johannes Gutenberg-University, 55131 Mainz, Germany; (P.S.H.); (T.K.)
- Department of Hematology & Medical Oncology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany;
| | - Udo F. Hartwig
- Department of Hematology & Medical Oncology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany;
- Research Center of Immunotherapy, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany
| | - Thomas Kindler
- University Cancer Center (UCT) Mainz, Johannes Gutenberg-University, 55131 Mainz, Germany; (P.S.H.); (T.K.)
- Department of Hematology & Medical Oncology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany;
| | - Leszek Wojnowski
- Department of Pharmacology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany; (J.A.M.N.); (U.G.-A.); (L.W.)
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28
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Chapdelaine AG, Sun G. Challenges and Opportunities in Developing Targeted Therapies for Triple Negative Breast Cancer. Biomolecules 2023; 13:1207. [PMID: 37627272 PMCID: PMC10452226 DOI: 10.3390/biom13081207] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/26/2023] [Accepted: 07/29/2023] [Indexed: 08/27/2023] Open
Abstract
Triple negative breast cancer (TNBC) is a heterogeneous group of breast cancers characterized by their lack of estrogen receptors, progesterone receptors, and the HER2 receptor. They are more aggressive than other breast cancer subtypes, with a higher mean tumor size, higher tumor grade, the worst five-year overall survival, and the highest rates of recurrence and metastasis. Developing targeted therapies for TNBC has been a major challenge due to its heterogeneity, and its treatment still largely relies on surgery, radiation therapy, and chemotherapy. In this review article, we review the efforts in developing targeted therapies for TNBC, discuss insights gained from these efforts, and highlight potential opportunities going forward. Accumulating evidence supports TNBCs as multi-driver cancers, in which multiple oncogenic drivers promote cell proliferation and survival. In such multi-driver cancers, targeted therapies would require drug combinations that simultaneously block multiple oncogenic drivers. A strategy designed to generate mechanism-based combination targeted therapies for TNBC is discussed.
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Affiliation(s)
| | - Gongqin Sun
- Department of Cell and Molecular Biology, University of Rhode Island, Kingston, RI 02881, USA;
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29
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Sun L, Mi K, Hou Y, Hui T, Zhang L, Tao Y, Liu Z, Huang L. Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications. Metabolites 2023; 13:897. [PMID: 37623842 PMCID: PMC10456269 DOI: 10.3390/metabo13080897] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023] Open
Abstract
Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.
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Affiliation(s)
- Lei Sun
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Kun Mi
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
| | - Yixuan Hou
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Tianyi Hui
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Lan Zhang
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Yanfei Tao
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Zhenli Liu
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
| | - Lingli Huang
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
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30
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Haji N, Faizi M, Koutentis PA, Carty MP, Aldabbagh F. Heterocyclic Iminoquinones and Quinones from the National Cancer Institute (NCI, USA) COMPARE Analysis. Molecules 2023; 28:5202. [PMID: 37446864 DOI: 10.3390/molecules28135202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/21/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
This review uses the National Cancer Institute (NCI) COMPARE program to establish an extensive list of heterocyclic iminoquinones and quinones with similarities in differential growth inhibition patterns across the 60-cell line panel of the NCI Developmental Therapeutics Program (DTP). Many natural products and synthetic analogues are revealed as potential NAD(P)H:quinone oxidoreductase 1 (NQO1) substrates, through correlations to dipyridoimidazo[5,4-f]benzimidazoleiminoquinone (DPIQ), and as potential thioredoxin reductase (TrxR) inhibitors, through correlations to benzo[1,2,4]triazin-7-ones and pleurotin. The strong correlation to NQO1 infers the enzyme has a major influence on the amount of the active compound with benzo[e]perimidines, phenoxazinones, benz[f]pyrido[1,2-a]indole-6,11-quinones, seriniquinones, kalasinamide, indolequinones, and furano[2,3-b]naphthoquinones, hypothesised as prodrugs. Compounds with very strong correlations to known TrxR inhibitors had inverse correlations to the expression of both reductase enzymes, NQO1 and TrxR, including naphtho[2,3-b][1,4]oxazepane-6,11-diones, benzo[a]carbazole-1,4-diones, pyranonaphthoquinones (including kalafungin, nanaomycin A, and analogues of griseusin A), and discorhabdin C. Quinoline-5,8-dione scaffolds based on streptonigrin and lavendamycin can correlate to either reductase. Inhibitors of TrxR are not necessarily (imino)quinones, e.g., parthenolides, while oxidising moieties are essential for correlations to NQO1, as with the mitosenes. Herein, an overview of synthetic methods and biological activity of each family of heterocyclic imino(quinone) is provided.
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Affiliation(s)
- Naemah Haji
- Department of Pharmacy, School of Life Sciences, Pharmacy and Chemistry, Kingston University, Penrhyn Road, Kingston upon Thames, London KT1 2EE, UK
| | - Masoma Faizi
- Department of Pharmacy, School of Life Sciences, Pharmacy and Chemistry, Kingston University, Penrhyn Road, Kingston upon Thames, London KT1 2EE, UK
| | | | - Michael P Carty
- School of Biological and Chemical Sciences, University of Galway, University Road, H91 TK33 Galway, Ireland
| | - Fawaz Aldabbagh
- Department of Pharmacy, School of Life Sciences, Pharmacy and Chemistry, Kingston University, Penrhyn Road, Kingston upon Thames, London KT1 2EE, UK
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Zhang H, Ek CH, Rattray M, Milo M. SynBa: improved estimation of drug combination synergies with uncertainty quantification. Bioinformatics 2023; 39:i121-i130. [PMID: 37387161 DOI: 10.1093/bioinformatics/btad240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION There exists a range of different quantification frameworks to estimate the synergistic effect of drug combinations. The diversity and disagreement in estimates make it challenging to determine which combinations from a large drug screening should be proceeded with. Furthermore, the lack of accurate uncertainty quantification for those estimates precludes the choice of optimal drug combinations based on the most favourable synergistic effect. RESULTS In this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations, so that actionable decisions can be derived from the model outputs. The actionability is enabled by incorporating the Hill equation into SynBa, so that the parameters representing the potency and the efficacy can be preserved. Existing knowledge may be conveniently inserted due to the flexibility of the prior, as shown by the empirical Beta prior defined for the normalized maximal inhibition. Through experiments on large combination screenings and comparison against benchmark methods, we show that SynBa provides improved accuracy of dose-response predictions and better-calibrated uncertainty estimation for the parameters and the predictions. AVAILABILITY AND IMPLEMENTATION The code for SynBa is available at https://github.com/HaotingZhang1/SynBa. The datasets are publicly available (DOI of DREAM: 10.7303/syn4231880; DOI of the NCI-ALMANAC subset: 10.5281/zenodo.4135059).
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Affiliation(s)
- Haoting Zhang
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, United Kingdom
- Health Data Research UK, London NW1 2BE, United Kingdom
| | - Carl Henrik Ek
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, United Kingdom
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester M13 9PL, United Kingdom
- Alan Turing Institute, London NW1 2DB, United Kingdom
| | - Marta Milo
- Oncology Data Science, Oncology R&D AstraZeneca, Cambridge CB2 8PA, United Kingdom
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32
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Shen F, Huang J, Yang K, Sun C. A Comprehensive Review of Interventional Clinical Trials in Patients with Bone Metastases. Onco Targets Ther 2023; 16:485-495. [PMID: 37408994 PMCID: PMC10318107 DOI: 10.2147/ott.s415399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/21/2023] [Indexed: 07/07/2023] Open
Abstract
Bone metastasis is one of the most important factors associated with poor prognosis for patients with prostate, breast, thyroid, and lung cancer. In the past two decades, 651 clinical trials, including 554 interventional trials, were being registered in ClinicalTrials.gov and pharma.id.informa.com to combat bone metastases from different perspectives. In this review, we comprehensively analyzed, regrouped, and discussed all the interventional trials on bone metastases. Clinical trials were re-grouped into bone-targeting agents, radiotherapy, small molecule targeted therapy, combination therapy, and others, based on the different mechanisms of action including modifying the bone microenvironment and preventing the growth of cancer cells. We also discussed the potential strategies that might improve overall survival and progression-free survival of patients with bone metastases in the future.
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Affiliation(s)
- Fei Shen
- Department of Orthopedics, Suzhou Wuzhong People’s Hospital, Suzhou, People’s Republic of China
| | - Jihe Huang
- Department of Orthopedics, Suzhou Wuzhong People’s Hospital, Suzhou, People’s Republic of China
| | - Kejia Yang
- Department of Orthopedics, Suzhou Wuzhong People’s Hospital, Suzhou, People’s Republic of China
| | - Chunhua Sun
- Department of Orthopedics, Suzhou Wuzhong People’s Hospital, Suzhou, People’s Republic of China
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Nair NU, Greninger P, Zhang X, Friedman AA, Amzallag A, Cortez E, Sahu AD, Lee JS, Dastur A, Egan RK, Murchie E, Ceribelli M, Crowther GS, Beck E, McClanaghan J, Klump-Thomas C, Boisvert JL, Damon LJ, Wilson KM, Ho J, Tam A, McKnight C, Michael S, Itkin Z, Garnett MJ, Engelman JA, Haber DA, Thomas CJ, Ruppin E, Benes CH. A landscape of response to drug combinations in non-small cell lung cancer. Nat Commun 2023; 14:3830. [PMID: 37380628 PMCID: PMC10307832 DOI: 10.1038/s41467-023-39528-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/14/2023] [Indexed: 06/30/2023] Open
Abstract
Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.
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Affiliation(s)
- Nishanth Ulhas Nair
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Xiaohu Zhang
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Adam A Friedman
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Arnaud Amzallag
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eliane Cortez
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Avinash Das Sahu
- University of New Mexico, Comprehensive Cancer Center, Albuquerque, NM, USA
| | - Joo Sang Lee
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, 16419, Republic of Korea
| | - Anahita Dastur
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Regina K Egan
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ellen Murchie
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Erin Beck
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | | | | | | | - Leah J Damon
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jeffrey Ho
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Angela Tam
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sam Michael
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Zina Itkin
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Mathew J Garnett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
| | | | - Daniel A Haber
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institute of Health, Rockville, MD, 20850, USA
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Cyril H Benes
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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34
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张 立, 秦 玉, 陈 明. [SMILESynergy: Anticancer drug synergy prediction based on Transformer pre-trained model]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2023; 40:544-551. [PMID: 37380395 PMCID: PMC10307610 DOI: 10.7507/1001-5515.202209043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 05/05/2023] [Indexed: 06/30/2023]
Abstract
The synergistic effect of drug combinations can solve the problem of acquired resistance to single drug therapy and has great potential for the treatment of complex diseases such as cancer. In this study, to explore the impact of interactions between different drug molecules on the effect of anticancer drugs, we proposed a Transformer-based deep learning prediction model-SMILESynergy. First, the drug text data-simplified molecular input line entry system (SMILES) were used to represent the drug molecules, and drug molecule isomers were generated through SMILES Enumeration for data augmentation. Then, the attention mechanism in the Transformer was used to encode and decode the drug molecules after data augmentation, and finally, a multi-layer perceptron (MLP) was connected to obtain the synergy value of the drugs. Experimental results showed that our model had a mean squared error of 51.34 in regression analysis, an accuracy of 0.97 in classification analysis, and better predictive performance than the DeepSynergy and MulinputSynergy models. SMILESynergy offers improved predictive performance to assist researchers in rapidly screening optimal drug combinations to improve cancer treatment outcomes.
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Affiliation(s)
- 立强 张
- 上海海洋大学 信息学院(上海 201306)College of Information, Shanghai Ocean University, Shanghai 201306, P. R. China
- 农业农村部渔业信息重点实验室(上海 201306)Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai 201306, P. R. China
| | - 玉芳 秦
- 上海海洋大学 信息学院(上海 201306)College of Information, Shanghai Ocean University, Shanghai 201306, P. R. China
- 农业农村部渔业信息重点实验室(上海 201306)Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai 201306, P. R. China
| | - 明 陈
- 上海海洋大学 信息学院(上海 201306)College of Information, Shanghai Ocean University, Shanghai 201306, P. R. China
- 农业农村部渔业信息重点实验室(上海 201306)Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai 201306, P. R. China
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35
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Reinhold WC, Wilson K, Elloumi F, Bradwell KR, Ceribelli M, Varma S, Wang Y, Duveau D, Menon N, Trepel J, Zhang X, Klumpp-Thomas C, Micheal S, Shinn P, Luna A, Thomas C, Pommier Y. CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations. Cancer Res 2023; 83:1941-1952. [PMID: 37140427 PMCID: PMC10330642 DOI: 10.1158/0008-5472.can-22-2996] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/27/2023] [Accepted: 04/25/2023] [Indexed: 05/05/2023]
Abstract
Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate these efforts, we have developed CellMinerCDB: National Center for Advancing Translational Sciences (NCATS; https://discover.nci.nih.gov/rsconnect/cellminercdb_ncats/), which makes available activity information for 2,675 drugs and compounds, including multiple nononcology drugs and 1,866 drugs and compounds unique to the NCATS. CellMinerCDB: NCATS comprises 183 cancer cell lines, with 72 unique to NCATS, including some from previously understudied tissues of origin. Multiple forms of data from different institutes are integrated, including single and combination drug activity, DNA copy number, methylation and mutation, transcriptome, protein levels, histone acetylation and methylation, metabolites, CRISPR, and miscellaneous signatures. Curation of cell lines and drug names enables cross-database (CDB) analyses. Comparison of the datasets is made possible by the overlap between cell lines and drugs across databases. Multiple univariate and multivariate analysis tools are built-in, including linear regression and LASSO. Examples have been presented here for the clinical topoisomerase I (TOP1) inhibitors topotecan and irinotecan/SN-38. This web application provides both substantial new data and significant pharmacogenomic integration, allowing exploration of interrelationships. SIGNIFICANCE CellMinerCDB: NCATS provides activity information for 2,675 drugs in 183 cancer cell lines and analysis tools to facilitate pharmacogenomic research and to identify determinants of response.
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Affiliation(s)
- William C. Reinhold
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Kelli Wilson
- National Center for Advancing Translational Sciences, NIH Bethesda, MD 20892, USA
| | - Fathi Elloumi
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | | | - Michele Ceribelli
- National Center for Advancing Translational Sciences, NIH Bethesda, MD 20892, USA
| | - Sudhir Varma
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
- HiThru Analytics LLC, Princeton, NJ 08540, USA
| | - Yanghsin Wang
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
- ICF International Inc., Fairfax, VA 22031, USA
| | - Damien Duveau
- National Center for Advancing Translational Sciences, NIH Bethesda, MD 20892, USA
| | - Nikhil Menon
- National Center for Advancing Translational Sciences, NIH Bethesda, MD 20892, USA
| | - Jane Trepel
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Xiaohu Zhang
- National Center for Advancing Translational Sciences, NIH Bethesda, MD 20892, USA
| | | | - Samuel Micheal
- National Center for Advancing Translational Sciences, NIH Bethesda, MD 20892, USA
| | - Paul Shinn
- National Center for Advancing Translational Sciences, NIH Bethesda, MD 20892, USA
| | - Augustin Luna
- cBio Center, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Craig Thomas
- National Center for Advancing Translational Sciences, NIH Bethesda, MD 20892, USA
| | - Yves Pommier
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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36
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Gross SM, Mohammadi F, Sanchez-Aguila C, Zhan PJ, Liby TA, Dane MA, Meyer AS, Heiser LM. Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects. Nat Commun 2023; 14:3450. [PMID: 37301933 PMCID: PMC10257663 DOI: 10.1038/s41467-023-39122-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.
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Affiliation(s)
- Sean M Gross
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Farnaz Mohammadi
- Department of Bioengineering, University of California, Los Angeles; Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, USA
| | - Crystal Sanchez-Aguila
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Paulina J Zhan
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Tiera A Liby
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Mark A Dane
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Aaron S Meyer
- Department of Bioengineering, University of California, Los Angeles; Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
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37
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Liu H, Fan Z, Lin J, Yang Y, Ran T, Chen H. The recent progress of deep-learning-based in silico prediction of drug combination. Drug Discov Today 2023:103625. [PMID: 37236526 DOI: 10.1016/j.drudis.2023.103625] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/24/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023]
Abstract
Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.
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Affiliation(s)
- Haoyang Liu
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; College of Life Sciences, Nankai University, Tianjin 300071, China
| | - Zhiguang Fan
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Jie Lin
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China.
| | - Ting Ran
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China.
| | - Hongming Chen
- Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China.
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38
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Ren X, Kang C, Garcia-Contreras L, Kim D. Understanding of Ovarian Cancer Cell-Derived Exosome Tropism for Future Therapeutic Applications. Int J Mol Sci 2023; 24:8166. [PMID: 37175872 PMCID: PMC10179437 DOI: 10.3390/ijms24098166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/18/2023] [Accepted: 04/30/2023] [Indexed: 05/15/2023] Open
Abstract
Exosomes, a subtype of extracellular vesicles, ranging from 50 to 200 nm in diameter, and mediate cell-to-cell communication in normal biological and pathological processes. Exosomes derived from tumors have multiple functions in cancer progression, resistance, and metastasis through cancer exosome-derived tropism. However, there is no quantitative information on cancer exosome-derived tropism. Such data would be highly beneficial to guide cancer therapy by inhibiting exosome release and/or uptake. Using two fluorescent protein (mKate2) transfected ovarian cancer cell lines (OVCA4 and OVCA8), cancer exosome tropism was quantified by measuring the released exosome from ovarian cancer cells and determining the uptake of exosomes into parental ovarian cancer cells, 3D spheroids, and tumors in tumor-bearing mice. The OVCA4 cells release 50 to 200 exosomes per cell, and the OVCA8 cells do 300 to 560 per cell. The uptake of exosomes by parental ovarian cancer cells is many-fold higher than by non-parental cells. In tumor-bearing mice, most exosomes are homing to the parent cancer rather than other tissues. We successfully quantified exosome release and uptake by the parent cancer cells, further proving the tropism of cancer cell-derived exosomes. The results implied that cancer exosome tropism could provide useful information for future cancer therapeutic applications.
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Affiliation(s)
- Xiaoyu Ren
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA; (X.R.); (C.K.); (L.G.-C.)
| | - Changsun Kang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA; (X.R.); (C.K.); (L.G.-C.)
| | - Lucila Garcia-Contreras
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA; (X.R.); (C.K.); (L.G.-C.)
| | - Dongin Kim
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA; (X.R.); (C.K.); (L.G.-C.)
- Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
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Sarmah D, Meredith WO, Weber IK, Price MR, Birtwistle MR. Predicting anti-cancer drug combination responses with a temporal cell state network model. PLoS Comput Biol 2023; 19:e1011082. [PMID: 37126527 PMCID: PMC10174488 DOI: 10.1371/journal.pcbi.1011082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 05/11/2023] [Accepted: 04/06/2023] [Indexed: 05/02/2023] Open
Abstract
Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging, even for simple in vitro systems. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines in vitro. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. While further application to different cell lines, more drugs, additional cell state networks, and more complex co-culture or in vivo systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination response for single cell lines in vitro.
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Affiliation(s)
- Deepraj Sarmah
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
| | - Wesley O. Meredith
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
| | - Ian K. Weber
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
- The University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Madison R. Price
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
- College of Pharmacy, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Marc R. Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina, United States of America
- Department of Bioengineering, Clemson University, Clemson, South Carolina, United States of America
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40
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Rønneberg L, Kirk PDW, Zucknick M. Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach. BMC Bioinformatics 2023; 24:161. [PMID: 37085771 PMCID: PMC10120211 DOI: 10.1186/s12859-023-05256-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/28/2023] [Indexed: 04/23/2023] Open
Abstract
In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.
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Affiliation(s)
- Leiv Rønneberg
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Paul D W Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- Ovarian Cancer Programme, Cancer Research UK Cambridge Centre, Cambridge, UK
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.
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Torkamannia A, Omidi Y, Ferdousi R. SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy. Sci Rep 2023; 13:6184. [PMID: 37061563 PMCID: PMC10105711 DOI: 10.1038/s41598-023-33271-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/11/2023] [Indexed: 04/17/2023] Open
Abstract
Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs.
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Affiliation(s)
- Anna Torkamannia
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, 51656/65811, Iran
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL, 33328, USA
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, 51656/65811, Iran.
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Zhang H, Wang Z, Nan Y, Zagidullin B, Yi D, Tang J, Guan Y. Harmonizing across datasets to improve the transferability of drug combination prediction. Commun Biol 2023; 6:397. [PMID: 37041243 PMCID: PMC10090076 DOI: 10.1038/s42003-023-04783-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/30/2023] [Indexed: 04/13/2023] Open
Abstract
Combination treatment has multiple advantages over traditional monotherapy in clinics, thus becoming a target of interest for many high-throughput screening (HTS) studies, which enables the development of machine learning models predicting the response of new drug combinations. However, most existing models have been tested only within a single study, and these models cannot generalize across different datasets due to significantly variable experimental settings. Here, we thoroughly assessed the transferability issue of single-study-derived models on new datasets. More importantly, we propose a method to overcome the experimental variability by harmonizing dose-response curves of different studies. Our method improves the prediction performance of machine learning models by 184% and 1367% compared to the baseline models in intra-study and inter-study predictions, respectively, and shows consistent improvement in multiple cross-validation settings. Our study addresses the crucial question of the transferability in drug combination predictions, which is fundamental for such models to be extrapolated to new drug combination discovery and clinical applications that are de facto different datasets.
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Affiliation(s)
- Hanrui Zhang
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ziyan Wang
- Department of Electrical Engineering and Computer Science (EECS) - CSE Division, University of Michigan, Ann Arbor, MI, USA
| | - Yiyang Nan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Bulat Zagidullin
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Daiyao Yi
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
- Department of Internal medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
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Baptista D, Ferreira PG, Rocha M. A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. PLoS Comput Biol 2023; 19:e1010200. [PMID: 36952569 PMCID: PMC10072473 DOI: 10.1371/journal.pcbi.1010200] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 04/04/2023] [Accepted: 02/08/2023] [Indexed: 03/25/2023] Open
Abstract
One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.
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Affiliation(s)
- Delora Baptista
- CEB - Centre of Biological Engineering, University of Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga, Guimarães, Portugal
| | - Pedro G Ferreira
- Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
- INESC TEC, Porto, Portugal
- Ipatimup - Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal
- i3s - Instituto de Investigação e Inovação em Saúde da Universidade do Porto, Porto, Portugal
| | - Miguel Rocha
- CEB - Centre of Biological Engineering, University of Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga, Guimarães, Portugal
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Balamurugan K, Poria DK, Sehareen SW, Krishnamurthy S, Tang W, McKennett L, Padmanaban V, Czarra K, Ewald AJ, Ueno NT, Ambs S, Sharan S, Sterneck E. Stabilization of E-cadherin adhesions by COX-2/GSK3β signaling is a targetable pathway in metastatic breast cancer. JCI Insight 2023; 8:156057. [PMID: 36757813 PMCID: PMC10070121 DOI: 10.1172/jci.insight.156057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 02/07/2023] [Indexed: 02/10/2023] Open
Abstract
Metastatic progression of epithelial cancers can be associated with epithelial-mesenchymal transition (EMT) including transcriptional inhibition of E-cadherin (CDH1) expression. Recently, EM plasticity (EMP) and E-cadherin-mediated, cluster-based metastasis and treatment resistance have become more appreciated. However, the mechanisms that maintain E-cadherin expression in this context are less understood. Through studies of inflammatory breast cancer (IBC) and a 3D tumor cell "emboli" culture paradigm, we discovered that cyclooxygenase 2 (COX-2; PTGS2), a target gene of C/EBPδ (CEBPD), or its metabolite prostaglandin E2 (PGE2) promotes protein stability of E-cadherin, β-catenin, and p120 catenin through inhibition of GSK3β. The COX-2 inhibitor celecoxib downregulated E-cadherin complex proteins and caused cell death. Coexpression of E-cadherin and COX-2 was seen in breast cancer tissues from patients with poor outcome and, along with inhibitory GSK3β phosphorylation, in patient-derived xenografts (PDX) including triple negative breast cancer (TNBC).Celecoxib alone decreased E-cadherin protein expression within xenograft tumors, though CDH1 mRNA levels increased, and reduced circulating tumor cell (CTC) clusters. In combination with paclitaxel, celecoxib attenuated or regressed lung metastases. This study has uncovered a mechanism by which metastatic breast cancer cells can maintain E-cadherin-mediated cell-to-cell adhesions and cell survival, suggesting that some patients with COX-2+/E-cadherin+ breast cancer may benefit from targeting of the PGE2 signaling pathway.
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Affiliation(s)
- Kuppusamy Balamurugan
- Laboratory of Cell and Developmental Signaling, Center for Cancer Research (CCR), National Cancer Institute (NCI), Frederick, Maryland, USA
| | - Dipak K Poria
- Laboratory of Cell and Developmental Signaling, Center for Cancer Research (CCR), National Cancer Institute (NCI), Frederick, Maryland, USA
| | - Saadiya W Sehareen
- Laboratory of Cell and Developmental Signaling, Center for Cancer Research (CCR), National Cancer Institute (NCI), Frederick, Maryland, USA
| | - Savitri Krishnamurthy
- Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Wei Tang
- Laboratory of Human Carcinogenesis, CCR, NCI, Bethesda, Maryland, USA
| | - Lois McKennett
- Laboratory Animal Sciences Program, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | - Veena Padmanaban
- Departments of Cell Biology and Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kelli Czarra
- Laboratory Animal Sciences Program, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA
| | - Andrew J Ewald
- Departments of Cell Biology and Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Naoto T Ueno
- Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, CCR, NCI, Bethesda, Maryland, USA
| | - Shikha Sharan
- Laboratory of Cell and Developmental Signaling, Center for Cancer Research (CCR), National Cancer Institute (NCI), Frederick, Maryland, USA
| | - Esta Sterneck
- Laboratory of Cell and Developmental Signaling, Center for Cancer Research (CCR), National Cancer Institute (NCI), Frederick, Maryland, USA
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45
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Berginski ME, Joisa CU, Golitz BT, Gomez SM. Kinome inhibition states and multiomics data enable prediction of cell viability in diverse cancer types. PLoS Comput Biol 2023; 19:e1010888. [PMID: 36809237 DOI: 10.1371/journal.pcbi.1010888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 03/03/2023] [Accepted: 01/20/2023] [Indexed: 02/23/2023] Open
Abstract
Protein kinases play a vital role in a wide range of cellular processes, and compounds that inhibit kinase activity emerging as a primary focus for targeted therapy development, especially in cancer. Consequently, efforts to characterize the behavior of kinases in response to inhibitor treatment, as well as downstream cellular responses, have been performed at increasingly large scales. Previous work with smaller datasets have used baseline profiling of cell lines and limited kinome profiling data to attempt to predict small molecule effects on cell viability, but these efforts did not use multi-dose kinase profiles and achieved low accuracy with very limited external validation. This work focuses on two large-scale primary data types, kinase inhibitor profiles and gene expression, to predict the results of cell viability screening. We describe the process by which we combined these data sets, examined their properties in relation to cell viability and finally developed a set of computational models that achieve a reasonably high prediction accuracy (R2 of 0.78 and RMSE of 0.154). Using these models, we identified a set of kinases, several of which are understudied, that are strongly influential in the cell viability prediction models. In addition, we also tested to see if a wider range of multiomics data sets could improve the model results and found that proteomic kinase inhibitor profiles were the single most informative data type. Finally, we validated a small subset of the model predictions in several triple-negative and HER2 positive breast cancer cell lines demonstrating that the model performs well with compounds and cell lines that were not included in the training data set. Overall, this result demonstrates that generic knowledge of the kinome is predictive of very specific cell phenotypes, and has the potential to be integrated into targeted therapy development pipelines.
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46
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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47
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Zhang P, Tu S, Zhang W, Xu L. Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism. Brief Bioinform 2022; 23:6711412. [PMID: 36136353 DOI: 10.1093/bib/bbac403] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/04/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022] Open
Abstract
Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.
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Affiliation(s)
- Peng Zhang
- Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shikui Tu
- Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wen Zhang
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Lei Xu
- Department of Computer Science and Engineering, Center for Cognitive Machines and Computational Health (CMaCH), Shanghai Jiao Tong University, Shanghai 200240, China
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48
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Madrid PB, Chang PY. Accelerating space radiation countermeasure development through drug repurposing. Life Sci Space Res (Amst) 2022; 35:30-35. [PMID: 36336366 DOI: 10.1016/j.lssr.2022.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/06/2022] [Accepted: 07/10/2022] [Indexed: 06/16/2023]
Abstract
The discovery of safe and effective radiation countermeasures (MCM) for long-duration spaceflight is challenging due to the complexity of the space radiation biology and high safety requirements. There are few if any clinically-validated molecular targets for this use case, and preclinical models have several known limitations. These challenges make the evaluation of existing FDA-approved drugs for this indication, or drug repurposing, an attractive strategy to accelerate space radiation countermeasure development. Drug repurposing offers several advantages over de novo drug discovery including established manufacturing methods, human clinical safety data, and well-understood dosing and pharmacokinetic considerations. There are limitations working with a fixed set of possible candidate compounds, but some properties of repurposed drugs can be tailored for well-defined new indications through reformulation and development of drug combinations. Drug repurposing is thus an attractive strategy for mitigating the high risks and costs of drug development and delivering new countermeasures to protect human from space radiation in long-term missions.
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Affiliation(s)
- P B Madrid
- SRI International, Biosciences Division, Menlo Park CA United States
| | - P Y Chang
- SRI International, Biosciences Division, Menlo Park CA United States.
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49
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Preto AJ, Matos-Filipe P, Mourão J, Moreira IS. SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning. Gigascience 2022; 11:6717722. [PMID: 36155782 PMCID: PMC9511701 DOI: 10.1093/gigascience/giac087] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 06/14/2022] [Accepted: 08/18/2022] [Indexed: 11/26/2022] Open
Abstract
Background In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)–powered informational view that integrates the relevant scientific fields and explores new territories. Results Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers. Conclusions The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.
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Affiliation(s)
- António J Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal.,PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Joana Mourão
- CNC-Center for Neuroscience and Cell Biology, CIBB-Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal
| | - Irina S Moreira
- Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal.,CNC-Center for Neuroscience and Cell Biology, CIBB-Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal
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50
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Rukhlenko OS, Halasz M, Rauch N, Zhernovkov V, Prince T, Wynne K, Maher S, Kashdan E, MacLeod K, Carragher NO, Kolch W, Kholodenko BN. Control of cell state transitions. Nature 2022; 609:975-985. [PMID: 36104561 PMCID: PMC9644236 DOI: 10.1038/s41586-022-05194-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 08/04/2022] [Indexed: 11/09/2022]
Abstract
Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here we present cell state transition assessment and regulation (cSTAR), an approach for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signalling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signalling and phenotypic data, cSTAR models how cells manoeuvre in Waddington's landscape1 and make decisions about which cell fate to adopt. Notably, cSTAR devises interventions to control the movement of cells in Waddington's landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets, including single-cell data, demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will enable designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.
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Affiliation(s)
- Oleksii S Rukhlenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Melinda Halasz
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Nora Rauch
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Vadim Zhernovkov
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Thomas Prince
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Kieran Wynne
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Stephanie Maher
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Eugene Kashdan
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
| | - Kenneth MacLeod
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Walter Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Boris N Kholodenko
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.
- Department of Pharmacology, Yale University School of Medicine, New Haven, USA.
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