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Huang Y, Chen X, Yang J, Yao Y, Wang M, Lu T, Li X, Wang J, Qiao S, Shi D, Li X, Li J, Xu Y. Novel Combination Therapy for Heart Failure: Trimebutine-Methoxsalen Identified through Synergistic Network Virtual Screening and Experimental Validation. J Chem Inf Model 2024. [PMID: 39259971 DOI: 10.1021/acs.jcim.4c00670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Combination therapy is increasingly favored by pharmaceutical companies and researchers as an effective way to quickly discover new drugs with excellent efficacy, especially in the treatment of complex diseases. Previously, we successfully developed a computational screening method to identify such combinations, although it fell short in elucidating their synergistic mechanisms. In this work, we have transitioned to a highest single agent (HSA) synergy model for network screening, which streamlines the discovery of promising combinations and facilitates the investigation of their synergistic effects. Through this refined approach, the trimebutine-methoxsalen combination emerged as a promising candidate for heart failure (HF) treatment, exhibiting significant in vitro cardioprotective effects and effectively mitigating isoproterenol (ISO)-induced structural remodeling in the mouse heart. Further mechanistic studies have demonstrated that trimebutine and methoxsalen could synergistically inhibit intracellular calcium overload in myocardial cells and reduce the production of ROS, thus exerting cardioprotective effects. Overall, this study introduces an advanced computational strategy that not only identifies a novel combination therapy against HF but also sheds light on its underlying synergistic mechanisms.
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Affiliation(s)
- Yunyuan Huang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei 430079, China
| | - Xin Chen
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
| | - Jin Yang
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yue Yao
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Manjiong Wang
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Taotao Lu
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Xiao Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Jiqun Wang
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Sicong Qiao
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Donglei Shi
- Key Laboratory of Tropical Biological Resources of Ministry of Education, College of Pharmacy, Hainan University, Haikou, Hainan 570228, China
| | - Xiaokang Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Jian Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
- Key Laboratory of Tropical Biological Resources of Ministry of Education, College of Pharmacy, Hainan University, Haikou, Hainan 570228, China
- Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832003, China
| | - Yixiang Xu
- State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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2
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Vis DJ, Jaaks P, Aben N, Coker EA, Barthorpe S, Beck A, Hall C, Hall J, Lightfoot H, Lleshi E, Mironenko T, Richardson L, Tolley C, Garnett MJ, Wessels LFA. A pan-cancer screen identifies drug combination benefit in cancer cell lines at the individual and population level. Cell Rep Med 2024; 5:101687. [PMID: 39168097 PMCID: PMC11384948 DOI: 10.1016/j.xcrm.2024.101687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/10/2024] [Accepted: 07/23/2024] [Indexed: 08/23/2024]
Abstract
Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce "efficacious combination benefit" (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers in vitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.
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Affiliation(s)
- Daniel J Vis
- Department of EEMCS, Delft University of Technology, the Netherlands
| | | | - Nanne Aben
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | | | | | | | | | - James Hall
- Wellcome Sanger Institute, Cambridge, UK
| | | | | | | | | | | | | | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands; Oncode Institute, Utrecht, the Netherlands.
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3
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Patterson SC, Pomeroy AE, Palmer AC. Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism. Mol Cancer Ther 2024; 23:995-1009. [PMID: 38530117 PMCID: PMC11219261 DOI: 10.1158/1535-7163.mct-23-0642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/30/2024] [Accepted: 03/19/2024] [Indexed: 03/27/2024]
Abstract
Most aggressive lymphomas are treated with combination chemotherapy, commonly as multiple cycles of concurrent drug administration. Concurrent administration is in theory optimal when combination therapies have synergistic (more than additive) drug interactions. We investigated pharmacodynamic interactions in the standard 4-drug "CHOP" regimen in peripheral T-cell lymphoma (PTCL) cell lines and found that CHOP consistently exhibits antagonism and not synergy. We tested whether staggered treatment schedules could improve tumor cell kill by avoiding antagonism, using in vitro models of concurrent or staggered treatments. Surprisingly, we observed that tumor cell kill is maximized by concurrent drug administration despite antagonistic drug-drug interactions. We propose that an ultrasensitive dose response, as described in radiology by the linear-quadratic (LQ) model, can reconcile these seemingly contradictory experimental observations. The LQ model describes the relationship between cell survival and dose, and in radiology has identified scenarios favoring hypofractionated radiotherapy-the administration of fewer large doses rather than multiple smaller doses. Specifically, hypofractionated treatment can be favored when cells require an accumulation of DNA damage, rather than a "single hit," to die. By adapting the LQ model to combination chemotherapy and accounting for tumor heterogeneity, we find that tumor cell kill is maximized by concurrent administration of multiple drugs, even when chemotherapies have antagonistic interactions. Thus, our study identifies a new mechanism by which combination chemotherapy can be clinically beneficial that is not contingent on positive drug-drug interactions.
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Affiliation(s)
- Sarah C. Patterson
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Amy E. Pomeroy
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Adam C. Palmer
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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4
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors. Cancer Res 2024; 84:2021-2033. [PMID: 38581448 PMCID: PMC11178452 DOI: 10.1158/0008-5472.can-23-3005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/18/2023] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors. SIGNIFICANCE A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Robert F. Gruener
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Israa G. Abdelbar
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
- Clinical Pharmacy Practice Department, The British University in Egypt, El Sherouk, 11837, Egypt
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Anand G. Patel
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105
| | - R. Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
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5
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Sinha S, Vegesna R, Mukherjee S, Kammula AV, Dhruba SR, Wu W, Kerr DL, Nair NU, Jones MG, Yosef N, Stroganov OV, Grishagin I, Aldape KD, Blakely CM, Jiang P, Thomas CJ, Benes CH, Bivona TG, Schäffer AA, Ruppin E. PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. NATURE CANCER 2024; 5:938-952. [PMID: 38637658 DOI: 10.1038/s43018-024-00756-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.
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Affiliation(s)
- Sanju Sinha
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
- NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA.
| | - Rahulsimham Vegesna
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Sumit Mukherjee
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Ashwin V Kammula
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
- University of Maryland, College Park, MD, USA
| | | | - Wei Wu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - D Lucas Kerr
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Matthew G Jones
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
- Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA
- Whitehead Institute, Cambridge, MA, USA
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | | | - Ivan Grishagin
- Rancho BioSciences, San Diego, CA, USA
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Collin M Blakely
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Peng Jiang
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
- Lymphoid Malignancies Branch, 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
| | - Trever G Bivona
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA
| | | | - Eytan Ruppin
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, USA.
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6
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Mirzaie M, Gholizadeh E, Miettinen JJ, Ianevski F, Ruokoranta T, Saarela J, Manninen M, Miettinen S, Heckman CA, Jafari M. Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling. Oncogenesis 2024; 13:11. [PMID: 38429288 PMCID: PMC10907624 DOI: 10.1038/s41389-024-00510-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/01/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024] Open
Abstract
Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.
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Affiliation(s)
- Mehdi Mirzaie
- Department of Biochemistry and Developmental Biology, University of Helsinki, Helsinki, Finland
| | - Elham Gholizadeh
- Department of Biochemistry and Developmental Biology, University of Helsinki, Helsinki, Finland
| | - Juho J Miettinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Filipp Ianevski
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tanja Ruokoranta
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Jani Saarela
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Susanna Miettinen
- Adult Stem Cell Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Tays Research Services, Wellbeing Services County of Pirkanmaa, Tampere University Hospital, Tampere, Finland
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland - FIMM, HiLIFE - Helsinki Institute of Life Science, iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland.
| | - Mohieddin Jafari
- Department of Biochemistry and Developmental Biology, University of Helsinki, Helsinki, Finland.
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7
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Abd El-Hafeez T, Shams MY, Elshaier YAMM, Farghaly HM, Hassanien AE. Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs. Sci Rep 2024; 14:2428. [PMID: 38287066 PMCID: PMC10825182 DOI: 10.1038/s41598-024-52814-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/24/2024] [Indexed: 01/31/2024] Open
Abstract
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.
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Affiliation(s)
- Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt.
- Computer Science Unit, Deraya University, El-Minia, Egypt.
| | - Mahmoud Y Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Yaseen A M M Elshaier
- Department of Organic and Medicinal Chemistry, Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufia, Egypt
| | - Heba Mamdouh Farghaly
- Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt.
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8
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Larsson P, Pettersson D, Olsson M, Sarathchandra S, Abramsson A, Zetterberg H, Ittner E, Forssell-Aronsson E, Kovács A, Karlsson P, Helou K, Parris TZ. Repurposing proteasome inhibitors for improved treatment of triple-negative breast cancer. Cell Death Discov 2024; 10:57. [PMID: 38286854 PMCID: PMC10825133 DOI: 10.1038/s41420-024-01819-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 01/10/2024] [Accepted: 01/15/2024] [Indexed: 01/31/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is associated with poor prognosis and limited treatment options due to the lack of important receptors (estrogen receptor [ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]) used for targeted therapy. However, high-throughput in vitro drug screening of cell lines is a powerful tool for identifying effective drugs for a disease. Here, we determine the intrinsic chemosensitivity of TNBC cell lines to proteasome inhibitors (PIs), thereby identifying potentially potent 2-drug combinations for TNBC. Eight TNBC cell lines (BT-549, CAL-148, HCC1806, HCC38, HCC70, MDA-MB-436, MDA-MB-453, and MDA-MB-468) and two controls (MCF-10A and MCF-7) were first exposed to 18 drugs (11 PIs and 7 clinically relevant chemotherapeutic agents) as monotherapy, followed by prediction of potent 2-drug combinations using the IDACombo pipeline. The synergistic effects of the 2-drug combinations were evaluated with SynergyFinder in four TNBC cell lines (CAL-148, HCC1806, HCC38, and MDA-MB-468) and three controls (BT-474, MCF-7, and T47D) in vitro, followed by further evaluation of tumor regression in zebrafish tumor models established using HCC1806 and MCF-7 cells. Monotherapy identified nine effective drugs (bortezomib, carfilzomib, cisplatin, delanzomib, docetaxel, epoxomicin, MLN-2238, MLN-9708, and nedaplatin) across all cell lines. PIs (e.g., bortezomib, delanzomib, and epoxomicin) were highly potent drugs in TNBC cells, of which bortezomib and delanzomib inhibited the chymotrypsin-like activity of the 20 S proteasome by 100% at 10 µM. Moreover, several potent 2-drug combinations (e.g., bortezomib+nedaplatin and epoxomicin+epirubicin) that killed virtually 100% of cells were also identified. Although HCC1806- and MCF-7-derived xenografts treated with bortezomib+nedaplatin and carboplatin+paclitaxel were smaller, HCC1806 cells frequently metastasized to the trunk region. Taken together, we show that PIs used in combination with platinum agents or topoisomerase inhibitors exhibit increased efficiency with almost 100% inhibition in TNBC cell lines, indicating that PIs are therefore promising compounds to use as combination therapy for TNBC.
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Affiliation(s)
- Peter Larsson
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniella Pettersson
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Maxim Olsson
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | | | - Alexandra Abramsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Dementia Research Institute, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Ella Ittner
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eva Forssell-Aronsson
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Anikó Kovács
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Per Karlsson
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Oncology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Khalil Helou
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Toshima Z Parris
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
- Sahlgrenska Center for Cancer Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
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9
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Li K, Huang Z, Xie G, Huang B, Song L, Zhang Y, Yang J. Transcriptomic insights into UTUC: role of inflammatory fibrosis and potential for personalized treatment. J Transl Med 2024; 22:24. [PMID: 38183115 PMCID: PMC10768331 DOI: 10.1186/s12967-023-04815-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/18/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Upper tract urothelial carcinoma (UTUC) is a rare disease, belonging to the same category of urothelial cancers as bladder cancer (BC). Despite sharing similar non-surgical treatment modalities, UTUC demonstrates a higher metastasis propensity than BC. Furthermore, although both cancers exhibit similar molecular disease emergence mechanisms, sequencing data reveals some differences. Our study investigates the transcriptomic distinctions between UTUC and BC, explores the causes behind UTUC's heightened metastatic tendency, constructs a model for UTUC metastasis and prognosis, and propose personalized treatment strategies for UTUC. METHODS In our research, we utilized differential gene expression analysis, interaction networks, and Cox regression to explore the enhanced metastatic propensity of UTUC. We formulated and validated a prognostic risk model using diverse techniques, including cell co-culture, reverse transcription quantitative polymerase chain reaction (rt-qPCR), western blotting, and transwell experiments. Our methodological approach also involved survival analysis, risk model construction, and drug screening leveraging the databases of CTRPv2, PRISM and CMap. We used the Masson staining technique for histological assessments. All statistical evaluations were conducted using R software and GraphPad Prism 9, reinforcing the rigorous and comprehensive nature of our research approach. RESULTS Screening through inflammatory fibrosis revealed a reduction of extracellular matrix and cell adhesion molecules regulated by proteoglycans in UTUC compared with BC, making UTUC more metastasis-prone. We demonstrated that SDC1, LUM, VEGFA, WNT7B, and TIMP3, are critical in promoting UTUC metastasis. A risk model based on these five molecules can effectively predict the risk of UTUC metastasis and disease-free survival time. Given UTUC's unique molecular mechanisms distinct from BC, we discovered that UTUC patients could better mitigate the issue of poor prognosis associated with UTUC's easy metastasis through tyrosine kinase inhibitors (TKIs) alongside the conventional gemcitabine and cisplatin chemotherapy regimen. CONCLUSIONS The poor prognosis of UTUC because of its high metastatic propensity is intimately tied to inflammatory fibrosis induced by the accumulation of reactive oxygen species. The biological model constructed using the five molecules SDC1, LUM, VEGFA, WNT7B, and TIMP3 can effectively predict patient prognosis. UTUC patients require specialized treatments in addition to conventional regimens, with TKIs exhibiting significant potential.
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Affiliation(s)
- Keqiang Li
- Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Zhenlin Huang
- Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Guoqing Xie
- Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Budeng Huang
- Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Liang Song
- Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Yu Zhang
- Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan Province, China.
| | - Jinjian Yang
- Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
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10
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Zhang J, Li C, Wang H, Yang Z, Hu C, Wu K, Hao J, Liu Z. Machine Learning-Assisted Automatically Electrochemical Addressable Cytosensing Arrays for Anticancer Drug Screening. Anal Chem 2023; 95:18907-18916. [PMID: 38088810 DOI: 10.1021/acs.analchem.3c05178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
The high-throughput and accurate screening of anticancer drugs is crucial to the preclinical assessment of candidate drugs and remains challenging. Herein, an automatically electrochemical addressable cytosensor (AEAC) for the efficient screening of anticancer drugs is reported. This sensor consists of sectionalized laser-induced graphene arrays decorated by the rhombohedral TiO2 and spherical Pt nanoparticles (LIG-TiO2-Pt) with high electrocatalytic activity for H2O2 and a homemade Ag/Pt electrode couple fixed onto the robot arm. The immobilization of laminin on the surface of LIG-TiO2-Pt can promote its biocompatibility for the growth and proliferation of various tumor cells, which empowers the in situ monitoring of H2O2 directly released from these live cells for drug screening. A machine learning (ML) algorithm is employed to eliminate the possible random or systematic errors of AEAC, realizing rapid, high-throughput, and accurate prediction of different types of anticancer drugs. This ML-assisted AEAC provides a powerful approach to accelerate the evolution of sensing-served tumor therapy.
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Affiliation(s)
- Jingwei Zhang
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Caoling Li
- Equine Science Research and Doping Control Center, Wuhan Business University, Wuhan 430056, China
| | - Han Wang
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Zhao Yang
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Chengguo Hu
- College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China
| | - Kangbing Wu
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Junxing Hao
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
| | - Zhihong Liu
- College of Health Science and Engineering, Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Hubei University, Wuhan 430062, China
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11
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Zamora PO, Altay G, Santamaria U, Dwarshuis N, Donthi H, Moon CI, Bakalar D, Zamora M. Drug Responses in Plexiform Neurofibroma Type I (PNF1) Cell Lines Using High-Throughput Data and Combined Effectiveness and Potency. Cancers (Basel) 2023; 15:5811. [PMID: 38136356 PMCID: PMC10742026 DOI: 10.3390/cancers15245811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/05/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Background: Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by heterozygous germline NF1 gene mutations that predispose patients to developing plexiform neurofibromas, which are benign but often disfiguring tumors of the peripheral nerve sheath induced by loss of heterozygosity at the NF1 locus. These can progress to malignant peripheral nerve sheath tumors (MPNSTs). There are no approved drug treatments for adults with NF1-related inoperable plexiform neurofibromas, and only one drug (selumetinib), which is an FDA-approved targeted therapy for the treatment of symptomatic pediatric plexiform neurofibromas, highlighting the need for additional drug screening and development. In high-throughput screening, the effectiveness of drugs against cell lines is often assessed by measuring in vitro potency (AC50) or the area under the curve (AUC). However, the variability of dose-response curves across drugs and cell lines and the frequency of partial effectiveness suggest that these measures alone fail to provide a full picture of overall efficacy. Methods: Using concentration-response data, we combined response effectiveness (EFF) and potency (AC50) into (a) a score characterizing the effect of a compound on a single cell line, S = log[EFF/AC50], and (b) a relative score, ΔS, characterizing the relative difference between a reference (e.g., non-tumor) and test (tumor) cell line. ΔS was applied to data from high-throughput screening (HTS) of a drug panel tested on NF1-/- tumor cells, using immortalized non-tumor NF1+/- cells as a reference. Results: We identified drugs with sensitivity, targeting expected pathways, such as MAPK-ERK and PI3K-AKT, as well as serotonin-related targets, among others. The ΔS technique used here, in tandem with a supplemental ΔS web tool, simplifies HTS analysis and may provide a springboard for further investigations into drug response in NF1-related cancers. The tool may also prove useful for drug development in a variety of other cancers.
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Affiliation(s)
| | | | | | | | | | - Chang In Moon
- Dan L. Duncan Comprehensive Cancer Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Lester and Sue Smith Breast Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Dana Bakalar
- National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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12
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Xia Y, Ling AL, Zhang W, Lee A, Su MC, Gruener RF, Jena S, Huang Y, Pareek S, Shan Y, Huang RS. A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo. JOURNAL OF CANCER SCIENCE AND CLINICAL THERAPEUTICS 2023; 7:253-258. [PMID: 38344217 PMCID: PMC10852200 DOI: 10.26502/jcsct.5079218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.
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Affiliation(s)
- Yunong Xia
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alexander L Ling
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
- Harvey Cushing Neuro-oncology Laboratories, Department of Neurosurgery, Hale Building for Transformative Medicine, 4th and 8th floor, Brigham and Women's Hospital; 60 Fenwood Road, Boston, MA 02116
| | - Weijie Zhang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mei-Chi Su
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Robert F Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Siddhika Pareek
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yuting Shan
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - R Stephanie Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
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13
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Ling AL, Zhang W, Lee A, Xia Y, Su MC, Gruener RF, Jena S, Huang Y, Pareek S, Shan Y, Stephanie Huang R. Simplicity: Web-Based Visualization and Analysis of High-Throughput Cancer Cell Line Screens. JOURNAL OF CANCER SCIENCE AND CLINICAL THERAPEUTICS 2023; 7:249-252. [PMID: 38435702 PMCID: PMC10906814 DOI: 10.26502/jcsct.5079217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatics skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high- throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.
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Affiliation(s)
- Alexander L Ling
- Harvey Cushing Neuro-oncology Laboratories, Department of Neurosurgery, Hale Building for Transformative Medicine, 4th and 8th floor, Brigham and Women's Hospital; 60 Fenwood Road, Boston, MA 02116
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Weijie Zhang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yunong Xia
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mei-Chi Su
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Robert F Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Siddhika Pareek
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yuting Shan
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - R Stephanie Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
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14
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Hwangbo H, Patterson SC, Dai A, Plana D, Palmer AC. Additivity predicts the efficacy of most approved combination therapies for advanced cancer. NATURE CANCER 2023; 4:1693-1704. [PMID: 37974028 DOI: 10.1038/s43018-023-00667-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 10/11/2023] [Indexed: 11/19/2023]
Abstract
Most advanced cancers are treated with drug combinations. Rational design aims to identify synergistic combinations, but existing synergy metrics apply to preclinical, not clinical data. Here we propose a model of drug additivity for progression-free survival (PFS) to assess whether clinical efficacies of approved drug combinations are additive or synergistic. This model includes patient-to-patient variability in best single-drug response plus the weaker drug per patient. Among US Food and Drug Administration approvals of drug combinations for advanced cancers (1995-2020), 95% exhibited additive or less than additive effects on PFS times. Among positive or negative phase 3 trials published between 2014-2018, every combination that improved PFS was expected to succeed by additivity (100% sensitivity) and most failures were expected to fail (78% specificity). This study shows synergy is neither a necessary nor common property of clinically effective drug combinations. The predictable efficacy of approved combinations suggests that additivity can be a design principle for combination therapies.
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Affiliation(s)
- Haeun Hwangbo
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sarah C Patterson
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Andy Dai
- North Carolina School of Science and Mathematics, Durham, NC, USA
| | - Deborah Plana
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School and MIT, Cambridge, MA, USA
| | - Adam C Palmer
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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15
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564598. [PMID: 37961545 PMCID: PMC10634928 DOI: 10.1101/2023.10.29.564598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Robert F Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Israa G Abdelbar
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
- Clinical Pharmacy Practice Department, The British University in Egypt, El Sherouk, 11837, Egypt
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Anand G Patel
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN 38105
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105
| | - R Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
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16
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Bertin P, Rector-Brooks J, Sharma D, Gaudelet T, Anighoro A, Gross T, Martínez-Peña F, Tang EL, Suraj MS, Regep C, Hayter JBR, Korablyov M, Valiante N, van der Sloot A, Tyers M, Roberts CES, Bronstein MM, Lairson LL, Taylor-King JP, Bengio Y. RECOVER identifies synergistic drug combinations in vitro through sequential model optimization. CELL REPORTS METHODS 2023; 3:100599. [PMID: 37797618 PMCID: PMC10626197 DOI: 10.1016/j.crmeth.2023.100599] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 10/07/2023]
Abstract
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased toward synergistic agents and results do not generalize out of distribution. During 5 rounds of experimentation, we employ sequential model optimization with a deep learning model to select drug combinations increasingly enriched for synergism and active against a cancer cell line-evaluating only ∼5% of the total search space. Moreover, we find that learned drug embeddings (using structural information) begin to reflect biological mechanisms. In silico benchmarking suggests search queries are ∼5-10× enriched for highly synergistic drug combinations by using sequential rounds of evaluation when compared with random selection or ∼3× when using a pretrained model.
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Affiliation(s)
- Paul Bertin
- Mila, the Quebec AI Institute, Montreal, QC, Canada
| | | | | | | | | | | | | | - Eileen L Tang
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
| | | | | | | | | | | | - Almer van der Sloot
- IRIC, Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada
| | - Mike Tyers
- Program in Molecular Medicine, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada
| | | | - Michael M Bronstein
- Relation Therapeutics, London, UK; Department of Computer Science, University of Oxford, Oxford, UK
| | - Luke L Lairson
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
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17
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Ling AL, Zhang W, Lee A, Xia Y, Su MC, Gruener RF, Jena S, Huang Y, Pareek S, Shan Y, Huang RS. Simplicity: web-based visualization and analysis of high-throughput cancer cell line screens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.08.556619. [PMID: 37745579 PMCID: PMC10515753 DOI: 10.1101/2023.09.08.556619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatic skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high-throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.
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Affiliation(s)
- Alexander L. Ling
- Harvey Cushing Neuro-oncology Laboratories, Department of Neurosurgery, Hale Building for Transformative Medicine, 4th and 8th floor, Brigham and Women’s Hospital; 60 Fenwood Road, Boston, MA 02116
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Weijie Zhang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yunong Xia
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mei-Chi Su
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Robert F. Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Siddhika Pareek
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Yuting Shan
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
| | - R. Stephanie Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
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18
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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] [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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19
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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: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [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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20
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Scoarta S, Küçükosmanoglu A, Bindt F, Pouwer M, Westerman BA. Review: A Roadmap to Use Nonstructured Data to Discover Multitarget Cancer Therapies. JCO Clin Cancer Inform 2023; 7:e2200096. [PMID: 37116097 PMCID: PMC10281332 DOI: 10.1200/cci.22.00096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 12/29/2022] [Accepted: 03/01/2023] [Indexed: 04/30/2023] Open
Abstract
Therapy resistance to single agents has led to the realization that combination therapies could become the cornerstone of cancer treatment. To operationalize the selection of effective and safe multitarget therapies, we propose to integrate chemical and preclinical therapeutic information with clinical efficacy and toxicity data, allowing a new perspective on the drug target landscape. To assess the feasibility of this approach, we evaluated the publicly available chemical, preclinical, and clinical therapeutic data, and we addressed some potential limitations while integrating the data. First, by mapping available structured data from the main biomedical resources, we noticed that there is only a 1.7% overlap between drugs in chemical, preclinical, or clinical databases. Especially, the limited amount of structured data in the clinical domain hinders linking drugs to clinical aspects such as efficacy and side effects. Second, to overcome the abovementioned knowledge gap between the chemical, preclinical, and clinical domain, we suggest information extraction from scientific literature and other unstructured resources through natural language processing models, where BioBERT and PubMedBERT are the current state-of-the-art approaches. Finally, we propose that knowledge graphs can be used to link structured data, scientific literature, and electronic health records, to come to meaningful interpretations. Together, we expect this richer knowledge will lower barriers toward clinical application of personalized combination therapies with high efficacy and limited adverse events.
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Affiliation(s)
- Silvia Scoarta
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, Amsterdam, the Netherlands
- The WINDOW Consortium, a collaboration between Amsterdam UMC, University of Birmingham, Birmingham, UK, and IOTA Pharmaceuticals, St Johns Innovation Centre, Cambridge, UK
| | - Asli Küçükosmanoglu
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, Amsterdam, the Netherlands
- The Toxicity-Atlas Consortium, a collaboration between Amsterdam UMC and Medstone, supported by the IKNL (Integrative Cancer-Center the Netherlands), Eindhoven, the Netherlands
| | - Felix Bindt
- Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, the Netherlands
| | - Marianne Pouwer
- The WINDOW Consortium, a collaboration between Amsterdam UMC, University of Birmingham, Birmingham, UK, and IOTA Pharmaceuticals, St Johns Innovation Centre, Cambridge, UK
- Medstone Science, Almere, the Netherlands
| | - Bart A. Westerman
- Department of Neurosurgery, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, Cancer Center Amsterdam, Amsterdam, the Netherlands
- The WINDOW Consortium, a collaboration between Amsterdam UMC, University of Birmingham, Birmingham, UK, and IOTA Pharmaceuticals, St Johns Innovation Centre, Cambridge, UK
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21
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Pomeroy AE, Schmidt EV, Sorger PK, Palmer AC. Drug independence and the curability of cancer by combination chemotherapy. Trends Cancer 2022; 8:915-929. [PMID: 35842290 PMCID: PMC9588605 DOI: 10.1016/j.trecan.2022.06.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/15/2022] [Accepted: 06/17/2022] [Indexed: 12/24/2022]
Abstract
Combination chemotherapy can cure certain leukemias and lymphomas, but most solid cancers are only curable at early stages. We review quantitative principles that explain the benefits of combining independently active cancer therapies in both settings. Understanding the mechanistic principles underlying curative treatments, including those developed many decades ago, is valuable for improving future combination therapies. We discuss contemporary evidence for long-established but currently neglected ideas of how combination therapy overcomes tumor heterogeneity. We show that a unified model of interpatient and intratumor heterogeneity describes historical progress in the treatment of pediatric acute lymphocytic leukemia (ALL), in which increasingly intensive combination regimens ultimately achieved high cure rates. We also describe three distinct aspects of drug independence that apply at different biological scales. The ability of these principles to quantitatively explain curative regimens suggests that supra-additive (synergistic) drug interactions are not required for successful combination therapy.
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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, NC, USA
| | - Emmett V Schmidt
- Oncology Early Development, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Peter K Sorger
- Harvard Ludwig Center and the Harvard Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Adam C Palmer
- Department of Pharmacology, Computational Medicine Program, UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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22
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Jiang P, Sinha S, Aldape K, Hannenhalli S, Sahinalp C, Ruppin E. Big data in basic and translational cancer research. Nat Rev Cancer 2022; 22:625-639. [PMID: 36064595 PMCID: PMC9443637 DOI: 10.1038/s41568-022-00502-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/26/2022] [Indexed: 02/07/2023]
Abstract
Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.
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Affiliation(s)
- Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Sanju Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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23
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Plasma Pharmacokinetics and Tissue Distribution of Doxorubicin in Rats following Treatment with Astragali Radix. Pharmaceuticals (Basel) 2022; 15:ph15091104. [PMID: 36145325 PMCID: PMC9505068 DOI: 10.3390/ph15091104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 08/29/2022] [Accepted: 09/01/2022] [Indexed: 11/17/2022] Open
Abstract
Doxorubicin (DOX) is an essential component in chemotherapy, and Astragali Radix (AR) is a widely used tonic herbal medicine. The combination of DOX and AR offers widespread, well-documented advantages in treating cancer, e.g., reducing the risk of adverse effects. This study mainly aims to uncover the impact of AR on DOX disposition in vivo. Rats received a single intravenous dose of 5 mg/kg DOX following a single-dose co-treatment or multiple-dose pre-treatment of AR (10 g/kg × 1 or × 10). The concentrations of DOX in rat plasma and six tissues, including heart, liver, lung, kidney, spleen, and skeletal muscle, were determined by a fully validated LC-MS/MS method. A network-based approach was further employed to quantify the relationships between enzymes that metabolize and transport DOX and the targets of nine representative AR components in the human protein−protein interactome. We found that short-term (≤10 d) AR administration was ineffective in changing the plasma pharmacokinetics of DOX in terms of the area under the concentration−time curve (AUC, 1303.35 ± 271.74 μg/L*h versus 1208.74 ± 145.35 μg/L*h, p > 0.46), peak concentrations (Cmax, 1351.21 ± 364.86 μg/L versus 1411.01 ± 368.38 μg/L, p > 0.78), and half-life (t1/2, 31.79 ± 5.12 h versus 32.05 ± 6.95 h, p > 0.94), etc. Compared to the isotype control group, DOX concentrations in six tissues slightly decreased under AR pre-administration but only showed statistical significance (p < 0.05) in the liver. Using network analysis, we showed that five of the nine representative AR components were not localized to the vicinity of the DOX disposition-associated module. These findings suggest that AR may mitigate DOX-induced toxicity by affecting drug targets rather than drug disposition.
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24
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Niu X, Lin L, Liu L, Yu Y, Wang H. Antifungal activity and molecular mechanisms of mulberrin derivatives against Colletotrichum gloeosporioides for mango storage. Int J Food Microbiol 2022; 378:109817. [PMID: 35759883 DOI: 10.1016/j.ijfoodmicro.2022.109817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/26/2022] [Accepted: 06/15/2022] [Indexed: 10/18/2022]
Abstract
In this work, by using high throughput virtual screening and bioactivity assays, this work revealed that three natural compounds, mulberrin (Mul) exhibiting the highest anti-CYP51 activity, isoxanthohumol and (s)-isopsoralen markedly inhibited 14α-demethylase (a pivotal biosynthetic enzyme involved in the biosynthesis of ergosterol) in Colletotrichum gloeosporioides. Results of computational biology analysis demonstrated that, among the three inhibitors bound to the catalytic pocket of CYP51, Mul showed a closer distance with heme in CYP51 and a stronger binding free energy with CYP51. In vitro tests, Mul demonstrated excellent anti-Colletotrichum gloeosporioides activity by inhibiting CYP51 activity. Notably, Mul treatment decreased the bioactivity of CYP51, thereby increasing cell membrane permeability and cell death. Moreover, Mul treatment significantly prolonged the preservation period of fruits. These results suggest that Mul suppresses anthracnose in postharvest mango by inhibiting the growth of Colletotrichum gloeosporioides and can be used as a potential natural preserving agent.
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Affiliation(s)
- Xiaodi Niu
- College of Food Science and Engineering, Jilin University, Changchun, China
| | - Li Lin
- College of Food Science and Engineering, Jilin University, Changchun, China
| | - Lu Liu
- College of Food Science and Engineering, Jilin University, Changchun, China
| | - Yiding Yu
- College of Food Science and Engineering, Jilin University, Changchun, China.
| | - Hongsu Wang
- College of Food Science and Engineering, Jilin University, Changchun, China.
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25
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Brown RM, Farouk Sait S, Dunn G, Sullivan A, Bruckert B, Sun D. Integrated Drug Mining Reveals Actionable Strategies Inhibiting Plexiform Neurofibromas. Brain Sci 2022; 12:brainsci12060720. [PMID: 35741605 PMCID: PMC9221468 DOI: 10.3390/brainsci12060720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Neurofibromatosis Type 1 (NF1) is one of the most common genetic tumor predisposition syndromes, affecting up to 1 in 2500 individuals. Up to half of patients with NF1 develop benign nerve sheath tumors called plexiform neurofibromas (PNs), characterized by biallelic NF1 loss. PNs can grow to immense sizes, cause extensive morbidity, and harbor a 15% lifetime risk of malignant transformation. Increasingly, molecular sequencing and drug screening data from various preclinical murine and human PN cell lines, murine models, and human PN tissues are available to help identify salient treatments for PNs. Despite this, Selumetinib, a MEK inhibitor, is the only currently FDA-approved pharmacotherapy for symptomatic and inoperable PNs in pediatric NF1 patients. The discovery of alternative and additional treatments has been hampered by the rarity of the disease, which makes prioritizing drugs to be tested in future clinical trials immensely important. Here, we propose a gene regulatory network-based integrated analysis to mine high-throughput cell line-based drug data combined with transcriptomes from resected human PN tumors. Conserved network modules were characterized and served as drug fingerprints reflecting the biological connections among drug effects and the inherent properties of PN cell lines and tissue. Drug candidates were ranked, and the therapeutic potential of drug combinations was evaluated via computational predication. Auspicious therapeutic agents and drug combinations were proposed for further investigation in preclinical and clinical trials.
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Affiliation(s)
- Rebecca M. Brown
- Medicine, Hematology and Medical Oncology, Neurosurgery, The Mount Sinai Hospital, New York, NY 10029, USA;
| | - Sameer Farouk Sait
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Griffin Dunn
- Department of Cell Biology, Neurobiology and Anatomy, The Medical College of Wisconsin, Milwaukee, WI 53226, USA; (G.D.); (A.S.); (B.B.)
| | - Alanna Sullivan
- Department of Cell Biology, Neurobiology and Anatomy, The Medical College of Wisconsin, Milwaukee, WI 53226, USA; (G.D.); (A.S.); (B.B.)
| | - Benjamin Bruckert
- Department of Cell Biology, Neurobiology and Anatomy, The Medical College of Wisconsin, Milwaukee, WI 53226, USA; (G.D.); (A.S.); (B.B.)
| | - Daochun Sun
- Department of Cell Biology, Neurobiology and Anatomy, The Medical College of Wisconsin, Milwaukee, WI 53226, USA; (G.D.); (A.S.); (B.B.)
- Department of Pediatrics, The Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Cancer Center, The Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Correspondence: ; Tel.: +1-414-955-8158
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26
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Liu L, Wang H, Lin L, Gao Y, Niu X. Mulberrin inhibits Botrytis cinerea for strawberry storage by interfering with the bioactivity of 14α-demethylase (CYP51). Food Funct 2022; 13:4032-4046. [PMID: 35315482 DOI: 10.1039/d2fo00295g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Currently, chemical agents hold great promise in preventing and combating Botrytis cinerea. However, the antifungal mechanism of some agents for B. cinerea remains rather vague, imposing restrictions on the research and development of novel antifungal inhibitors. In this work, we discovered that mulberrin (MBN), a natural compound from the root bark of Ramulus Mori, with an IC50 of 1.38 μM together, demonstrated marked anti-14α-demethylase (CYP51) activity through high throughput virtual screening and in vitro bioactivity assay. The computational biology results demonstrated that MBN and its derivatives were bound to the catalytic activity region of CYP51, but only MBN could form a strong π-cation interaction with the Fe ion of heme in CYP51 via the 2-methylpent-2-ene moiety at atom C9. MBN had a stronger binding free energy than the other three compounds with CYP51, implying that the 2-methylpent-2-ene moiety at atom C9 is a critical pharmacophore for CYP51 inhibitors. Subsequently, through an antifungal test, MBN demonstrated excellent anti-B. cinerea activity by inhibiting CYP51 activity. The EC50 values of MBN toward hyphal growth and spore germination in B. cinerea were 17.27 and 9.56 μg mL-1, respectively. The bioactivity loss of CYP51 by direct interaction with MBN induced the increase of cell membrane permeability, membrane destruction, and cell death. Meanwhile, in the B. cinerea infection model, MBN significantly prolonged the preservation of strawberries by preventing B. cinerea from infecting strawberries and could be used as a potential natural preserving agent for storing fruits.
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Affiliation(s)
- Lu Liu
- College of Food Science and Engineering, Jilin University, Changchun, China.
| | - Hongsu Wang
- College of Food Science and Engineering, Jilin University, Changchun, China.
| | - Li Lin
- College of Food Science and Engineering, Jilin University, Changchun, China.
| | - Yawen Gao
- College of Food Science and Engineering, Jilin University, Changchun, China.
| | - Xiaodi Niu
- College of Food Science and Engineering, Jilin University, Changchun, China.
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27
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Circadian and Immunity Cycle Talk in Cancer Destination: From Biological Aspects to In Silico Analysis. Cancers (Basel) 2022; 14:cancers14061578. [PMID: 35326729 PMCID: PMC8945968 DOI: 10.3390/cancers14061578] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary The circadian cycle is a natural cycle of the body repeated every 24 h, based on a day and night rhythm, and it affects many body processes. The present article reviews the importance and role of the circadian cycle in cancer and its association with the immune system and immunotherapy drugs at the cellular and molecular levels. It also examines the genes and cellular pathways involved in both circadian and immune systems. It offers possible computational solutions to increase the effectiveness of cancer treatment concerning the circadian cycle. Abstract Cancer is the leading cause of death and a major problem to increasing life expectancy worldwide. In recent years, various approaches such as surgery, chemotherapy, radiation, targeted therapies, and the newest pillar, immunotherapy, have been developed to treat cancer. Among key factors impacting the effectiveness of treatment, the administration of drugs based on the circadian rhythm in a person and within individuals can significantly elevate drug efficacy, reduce adverse effects, and prevent drug resistance. Circadian clocks also affect various physiological processes such as the sleep cycle, body temperature cycle, digestive and cardiovascular processes, and endocrine and immune systems. In recent years, to achieve precision patterns for drug administration using computational methods, the interaction of the effects of drugs and their cellular pathways has been considered more seriously. Integrated data-derived pathological images and genomics, transcriptomics, and proteomics analyses have provided an understanding of the molecular basis of cancer and dramatically revealed interactions between circadian and immunity cycles. Here, we describe crosstalk between the circadian cycle signaling pathway and immunity cycle in cancer and discuss how tumor microenvironment affects the influence on treatment process based on individuals’ genetic differences. Moreover, we highlight recent advances in computational modeling that pave the way for personalized immune chronotherapy.
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28
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Plana D, Palmer AC, Sorger PK. Independent Drug Action in Combination Therapy: Implications for Precision Oncology. Cancer Discov 2022; 12:606-624. [PMID: 34983746 DOI: 10.1158/2159-8290.cd-21-0212] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 09/02/2021] [Accepted: 11/10/2021] [Indexed: 11/16/2022]
Abstract
Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy.
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Affiliation(s)
- Deborah Plana
- Laboratory of Systems Pharmacology and the Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts
| | - 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
| | - Peter K Sorger
- Laboratory of Systems Pharmacology and the Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
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29
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Athanasiadis P, Ianevski A, Skånland SS, Aittokallio T. Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells. Methods Mol Biol 2022; 2449:327-348. [PMID: 35507270 DOI: 10.1007/978-1-0716-2095-3_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In many complex diseases, such as cancers, resistance to monotherapies easily occurs, and longer-term treatment responses often require combinatorial therapies as next-line regimens. However, due to a massive number of possible drug combinations to test, there is a need for systematic and rational approaches to finding safe and effective drug combinations for each individual patient. This protocol describes an ecosystem of computational methods to guide high-throughput combinatorial screening that help experimental researchers to identify optimal drug combinations in terms of synergy, efficacy, and/or selectivity for further preclinical and clinical investigation. The methods are demonstrated in the context of combinatorial screening in primary cells of leukemia patients, where the translational aim is to identify drug combinations that show not only high synergy but also maximal cancer-selectivity. The mechanism-agnostic and cost-effective computational methods are widely applicable to various cancer types, which are amenable to drug testing, as the computational methods take as input only the phenotypic measurements of a subset of drug combinations, without requiring target information or genomic profiles of the patient samples.
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Affiliation(s)
- Paschalis Athanasiadis
- Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - Aleksandr Ianevski
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Sigrid S Skånland
- Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tero Aittokallio
- Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
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30
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Kong W, Midena G, Chen Y, Athanasiadis P, Wang T, Rousu J, He L, Aittokallio T. Systematic review of computational methods for drug combination prediction. Comput Struct Biotechnol J 2022; 20:2807-2814. [PMID: 35685365 PMCID: PMC9168078 DOI: 10.1016/j.csbj.2022.05.055] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/27/2022] [Accepted: 05/27/2022] [Indexed: 12/26/2022] Open
Abstract
Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.
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31
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Network Biology and Artificial Intelligence Drive the Understanding of the Multidrug Resistance Phenotype in Cancer. Drug Resist Updat 2022; 60:100811. [DOI: 10.1016/j.drup.2022.100811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/22/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023]
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32
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Davis K, Greenstein T, Viau Colindres R, Aldridge BB. Leveraging laboratory and clinical studies to design effective antibiotic combination therapy. Curr Opin Microbiol 2021; 64:68-75. [PMID: 34628295 PMCID: PMC8671129 DOI: 10.1016/j.mib.2021.09.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/28/2021] [Accepted: 09/16/2021] [Indexed: 01/21/2023]
Abstract
Interest in antibiotic combination therapy is increasing due to antimicrobial resistance and a slowing antibiotic pipeline. However, aside from specific indications, combination therapy in the clinic is often not administered systematically; instead, it is used at the physician's discretion as a bet-hedging mechanism to increase the chances of appropriately targeting a pathogen(s) with an unknown antibiotic resistance profile. Some recent clinical trials have been unable to demonstrate superior efficacy of combination therapy over monotherapy. Other trials have shown a benefit of combination therapy in defined circumstances consistent with recent studies indicating that factors including species, strain, resistance profile, and microenvironment affect drug combination efficacy and drug interactions. In this review, we discuss how a careful study design that takes these factors into account, along with the different drug interaction and potency metrics for assessing combination performance, may provide the necessary insight to understand the best clinical use-cases for combination therapy.
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Affiliation(s)
- Kathleen Davis
- Department of Molecular Biology & Microbiology, Tufts University School of Medicine, United States; Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, United States
| | - Talia Greenstein
- Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, United States; Graduate School of Biomedical Sciences, Tufts University School of Medicine, United States
| | - Roberto Viau Colindres
- Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, United States; Department of Geographic Medicine and Infectious Diseases, Tufts Medical Center, United States
| | - Bree B Aldridge
- Department of Molecular Biology & Microbiology, Tufts University School of Medicine, United States; Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, United States; Graduate School of Biomedical Sciences, Tufts University School of Medicine, United States
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33
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Fan K, Cheng L, Li L. Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects. Brief Bioinform 2021; 22:bbab271. [PMID: 34347041 PMCID: PMC8574962 DOI: 10.1093/bib/bbab271] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/04/2021] [Accepted: 06/25/2021] [Indexed: 12/27/2022] Open
Abstract
Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less toxicity and adverse side effects. However, it is infeasible to experimentally screen the enormous search space of all possible drug combinations. Therefore, developing computational models to efficiently and accurately identify potential anti-cancer synergistic drug combinations has attracted a lot of attention from the scientific community. Hypothesis-driven explicit mathematical methods or network pharmacology models have been popular in the last decade and have been comprehensively reviewed in previous surveys. With the surge of artificial intelligence and greater availability of large-scale datasets, machine learning especially deep learning methods are gaining popularity in the field of computational models for anti-cancer drug synergy prediction. Machine learning-based methods can be derived without strong assumptions about underlying mechanisms and have achieved state-of-the-art prediction performances, promoting much greater growth of the field. Here, we present a structured overview of available large-scale databases and machine learning especially deep learning methods in computational predictive models for anti-cancer drug synergy prediction. We provide a unified framework for machine learning models and detail existing model architectures as well as their contributions and limitations, shedding light into the future design of computational models. Besides, unbiased experiments are conducted to provide in-depth comparisons between reviewed papers in terms of their prediction performance.
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Affiliation(s)
- Kunjie Fan
- Department of Biomedical Informatics of The Ohio State University, 43202 Columbus, OH, USA
| | - Lijun Cheng
- Department of Biomedical Informatics of The Ohio State University, 43202 Columbus, OH, USA
| | - Lang Li
- Department of Biomedical Informatics of The Ohio State University, 43202 Columbus, OH, USA
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He L, Bulanova D, Oikkonen J, Häkkinen A, Zhang K, Zheng S, Wang W, Erkan EP, Carpén O, Joutsiniemi T, Hietanen S, Hynninen J, Huhtinen K, Hautaniemi S, Vähärautio A, Tang J, Wennerberg K, Aittokallio T. Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer. Brief Bioinform 2021; 22:bbab272. [PMID: 34343245 PMCID: PMC8574973 DOI: 10.1093/bib/bbab272] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/30/2021] [Accepted: 06/25/2021] [Indexed: 02/05/2023] Open
Abstract
Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.
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Affiliation(s)
- Liye He
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Daria Bulanova
- Biotech Research & Innovation Centre (BRIC) at the University of Copenhagen (UC), Helsinki, Finland
| | | | | | | | - Shuyu Zheng
- ONCOSYS Research Program in UH, Helsinki, Finland
| | - Wenyu Wang
- ONCOSYS Research Program in UH, Helsinki, Finland
| | | | - Olli Carpén
- ONCOSYS Research Program in UH, Helsinki, Finland
| | - Titta Joutsiniemi
- Gynecologic oncology in Turku University Hospital, Helsinki, Finland
| | - Sakari Hietanen
- ONCOSYS Research Program in UH and in University of Turku (UTU), Helsinki, Finland
| | | | | | | | | | - Jing Tang
- ONCOSYS Research Programme in UH, Helsinki, Finland
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Hot or cold: Bioengineering immune contextures into in vitro patient-derived tumor models. Adv Drug Deliv Rev 2021; 175:113791. [PMID: 33965462 DOI: 10.1016/j.addr.2021.05.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/02/2021] [Accepted: 05/04/2021] [Indexed: 02/06/2023]
Abstract
In the past decade, immune checkpoint inhibitors (ICI) have proven to be tremendously effective for a subset of cancer patients. However, it is difficult to predict the response of individual patients and efforts are now directed at understanding the mechanisms of ICI resistance. Current models of patient tumors poorly recapitulate the immune contexture, which describe immune parameters that are associated with patient survival. In this Review, we discuss parameters that influence the induction of different immune contextures found within tumors and how engineering strategies may be leveraged to recapitulate these contextures to develop the next generation of immune-competent patient-derived in vitro models.
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Zheng S, Aldahdooh J, Shadbahr T, Wang Y, Aldahdooh D, Bao J, Wang W, Tang J. DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal. Nucleic Acids Res 2021; 49:W174-W184. [PMID: 34060634 PMCID: PMC8218202 DOI: 10.1093/nar/gkab438] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/18/2021] [Accepted: 05/06/2021] [Indexed: 02/06/2023] Open
Abstract
Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.
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Affiliation(s)
- Shuyu Zheng
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki FI-00290, Finland
| | - Jehad Aldahdooh
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki FI-00290, Finland
| | - Tolou Shadbahr
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki FI-00290, Finland
| | - Yinyin Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki FI-00290, Finland
| | - Dalal Aldahdooh
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki FI-00290, Finland
| | - Jie Bao
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki FI-00290, Finland
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki FI-00290, Finland
| | - Wenyu Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki FI-00290, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki FI-00290, Finland
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Huang Y, Wang J, Liu J, Shi D, Li X, Wang M, Lu T, Jiang B, Xia C, Lin H, Xu Y, Li J. Rapid Repurposing of Novel Combination Drugs for the Treatment of Heart Failure via a Computationally Guided Network Screening Approach. J Chem Inf Model 2021; 62:5223-5232. [PMID: 34151561 DOI: 10.1021/acs.jcim.1c00132] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Combination drugs, characterized by high efficacy and few side effects, have received extensive attention from pharmaceutical companies and researchers for the treatment of complex diseases such as heart failure (HF). Traditional combination drug discovery depends on large-scale high-throughput experimental approaches that are time-consuming and costly. Herein we developed a novel, rapid, and potentially universal computer-guided combination drug-network-screening approach based on a set of databases and web services that are easy for individuals to obtain and operate, and we discovered for the first time that the menthol-allethrin combination screened by this approach exhibited a significant synergistic cardioprotective effect in vitro. Further mechanistic studies indicated that allethrin and menthol could synergistically block calcium channels. Allethrin bound to the central cavity of the voltage-dependent L-type calcium channel subunit alpha-1S (CACNA1S) lead to a conformational change in an allosteric site of CACNA1S, thereby enhancing the binding of menthol to this allosteric site. In summary, we reported a potentially universal computational approach to combination drug screening that has been used to discover a new combination of menthol-allethrin against HF in vitro, providing a new synergistic mechanism and prospective agent for HF treatment.
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Affiliation(s)
- Yunyuan Huang
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China
| | - Jiqun Wang
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China
| | - Jiaqi Liu
- Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Donglei Shi
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China
| | - Xiaokang Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China
| | - Manjiong Wang
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China
| | - Taotao Lu
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China
| | - Bei Jiang
- College of Pharmacy and Chemistry, Dali University, 5 Xue Ren Road, Dali 671000, China
| | - Conglong Xia
- College of Pharmacy and Chemistry, Dali University, 5 Xue Ren Road, Dali 671000, China
| | - Houwen Lin
- Research Center for Marine Drugs, State Key Laboratory of Oncogenes and Related Genes, Department of Pharmacy, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200127, China
| | - Yixiang Xu
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China
| | - Jian Li
- State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China.,College of Pharmacy and Chemistry, Dali University, 5 Xue Ren Road, Dali 671000, China.,Clinical Medicine Scientific and Technical Innovation Center, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200092, China
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