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Howell R, Davies J, Clarke MA, Appios A, Mesquita I, Jayal Y, Ringham-Terry B, Boned Del Rio I, Fisher J, Bennett CL. Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling. SCIENCE ADVANCES 2023; 9:eadd1992. [PMID: 37043573 PMCID: PMC10096595 DOI: 10.1126/sciadv.add1992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
While skin is a site of active immune surveillance, primary melanomas often escape detection. Here, we have developed an in silico model to determine the local cross-talk between melanomas and Langerhans cells (LCs), the primary antigen-presenting cells at the site of melanoma development. The model predicts that melanomas fail to activate LC migration to lymph nodes until tumors reach a critical size, which is determined by a positive TNF-α feedback loop within melanomas, in line with our observations of murine tumors. In silico drug screening, supported by subsequent experimental testing, shows that treatment of primary tumors with MAPK pathway inhibitors may further prevent LC migration. In addition, our in silico model predicts treatment combinations that bypass LC dysfunction. In conclusion, our combined approach of in silico and in vivo studies suggests a molecular mechanism that explains how early melanomas develop under the radar of immune surveillance by LC.
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Affiliation(s)
| | | | - Matthew A. Clarke
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Anna Appios
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Inês Mesquita
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Yashoda Jayal
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Ben Ringham-Terry
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
| | - Isabel Boned Del Rio
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, UK
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2
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Howell R, Clarke MA, Reuschl AK, Chen T, Abbott-Imboden S, Singer M, Lowe DM, Bennett CL, Chain B, Jolly C, Fisher J. Executable network of SARS-CoV-2-host interaction predicts drug combination treatments. NPJ Digit Med 2022; 5:18. [PMID: 35165389 PMCID: PMC8844383 DOI: 10.1038/s41746-022-00561-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 01/07/2022] [Indexed: 12/15/2022] Open
Abstract
The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic.
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3
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Myers PJ, Lee SH, Lazzara MJ. MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 28:100349. [PMID: 35935921 PMCID: PMC9348571 DOI: 10.1016/j.coisb.2021.05.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.
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Affiliation(s)
- Paul J. Myers
- Department of Chemical Engineering, Charlottesville, VA 22904
| | - Sung Hyun Lee
- Department of Chemical Engineering, Charlottesville, VA 22904
| | - Matthew J. Lazzara
- Department of Chemical Engineering, Charlottesville, VA 22904
- Department of Biomedical Engineering University of Virginia, Charlottesville, VA 22904
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4
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Gabor A, Tognetti M, Driessen A, Tanevski J, Guo B, Cao W, Shen H, Yu T, Chung V, Bodenmiller B, Saez‐Rodriguez J. Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd. Mol Syst Biol 2021; 17:e10402. [PMID: 34661974 PMCID: PMC8522707 DOI: 10.15252/msb.202110402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/30/2022] Open
Abstract
Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.
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Affiliation(s)
- Attila Gabor
- Institute for Computational BiomedicineHeidelberg University and Heidelberg University HospitalFaculty of MedicineBioquantHeidelbergGermany
| | - Marco Tognetti
- Department of Quantitative Biomedicine & Institute of Molecular Life SciencesUniversity of ZurichZurichSwitzerland
- Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Molecular Life Science PhD ProgramLife Science Zurich Graduate SchoolETH Zurich and University of ZurichZurichSwitzerland
| | - Alice Driessen
- Institute for Computational BiomedicineHeidelberg University and Heidelberg University HospitalFaculty of MedicineBioquantHeidelbergGermany
| | - Jovan Tanevski
- Institute for Computational BiomedicineHeidelberg University and Heidelberg University HospitalFaculty of MedicineBioquantHeidelbergGermany
| | - Baosen Guo
- Division of AI & BioinformaticsShenzhen Digital Life InstituteShenzhenChina
| | - Wencai Cao
- Division of AI & BioinformaticsShenzhen Digital Life InstituteShenzhenChina
| | - He Shen
- Division of AI & BioinformaticsShenzhen Digital Life InstituteShenzhenChina
| | | | | | | | - Bernd Bodenmiller
- Department of Quantitative Biomedicine & Institute of Molecular Life SciencesUniversity of ZurichZurichSwitzerland
| | - Julio Saez‐Rodriguez
- Institute for Computational BiomedicineHeidelberg University and Heidelberg University HospitalFaculty of MedicineBioquantHeidelbergGermany
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5
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Kiwumulo HF, Muwonge H, Ibingira C, Kirabira JB, Ssekitoleko RT. A systematic review of modeling and simulation approaches in designing targeted treatment technologies for Leukemia Cancer in low and middle income countries. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:8149-8173. [PMID: 34814293 DOI: 10.3934/mbe.2021404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Virtual experimentation is a widely used approach for predicting systems behaviour especially in situations where resources for physical experiments are very limited. For example, targeted treatment inside the human body is particularly challenging, and as such, modeling and simulation is utilised to aid planning before a specific treatment is administered. In such approaches, precise treatment, as it is the case in radiotherapy, is used to administer a maximum dose to the infected regions while minimizing the effect on normal tissue. Complicated cancers such as leukemia present even greater challenges due to their presentation in liquid form and not being localised in one area. As such, science has led to the development of targeted drug delivery, where the infected cells can be specifically targeted anywhere in the body. Despite the great prospects and advances of these modeling and simulation tools in the design and delivery of targeted drugs, their use by Low and Middle Income Countries (LMICs) researchers and clinicians is still very limited. This paper therefore reviews the modeling and simulation approaches for leukemia treatment using nanoparticles as an example for virtual experimentation. A systematic review from various databases was carried out for studies that involved cancer treatment approaches through modeling and simulation with emphasis to data collected from LMICs. Results indicated that whereas there is an increasing trend in the use of modeling and simulation approaches, their uptake in LMICs is still limited. According to the review data collected, there is a clear need to employ these tools as key approaches for the planning of targeted drug treatment approaches.
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Affiliation(s)
| | - Haruna Muwonge
- Department of Medical Physiology, Makerere University, Kampala, Uganda
| | - Charles Ibingira
- Department of Human Anatomy, Makerere University, Kampala, Uganda
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6
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Rathi A, Kumar D, Hasan GM, Haque MM, Hassan MI. Therapeutic targeting of PIM KINASE signaling in cancer therapy: Structural and clinical prospects. Biochim Biophys Acta Gen Subj 2021; 1865:129995. [PMID: 34455019 DOI: 10.1016/j.bbagen.2021.129995] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/28/2021] [Accepted: 08/23/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND PIM kinases are well-studied drug targets for cancer, belonging to Serine/Threonine kinases family. They are the downstream target of various signaling pathways, and their up/down-regulation affects various physiological processes. PIM family comprises three isoforms, namely, PIM-1, PIM-2, and PIM-3, on alternative initiation of translation and they have different levels of expression in different types of cancers. Its structure shows a unique ATP-binding site in the hinge region which makes it unique among other kinases. SCOPE OF REVIEW PIM kinases are widely reported in hematological malignancies along with prostate and breast cancers. Currently, many drugs are used as inhibitors of PIM kinases. In this review, we highlighted the physiological significance of PIM kinases in the context of disease progression and therapeutic targeting. We comprehensively reviewed the PIM kinases in terms of their expression and regulation of different physiological roles. We further predicted functional partners of PIM kinases to elucidate their role in the cellular physiology of different cancer and mapped their interaction network. MAJOR CONCLUSIONS A deeper mechanistic insight into the PIM signaling involved in regulating different cellular processes, including transcription, apoptosis, cell cycle regulation, cell proliferation, cell migration and senescence, is provided. Furthermore, structural features of PIM have been dissected to understand the mechanism of inhibition and subsequent implication of designed inhibitors towards therapeutic management of prostate, breast and other cancers. GENERAL SIGNIFICANCE Being a potential drug target for cancer therapy, available drugs and PIM inhibitors at different stages of clinical trials are discussed in detail.
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Affiliation(s)
- Aanchal Rathi
- Department of Biotechnology, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
| | - Dhiraj Kumar
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
| | - Gulam Mustafa Hasan
- Department of Biochemistry, College of Medicine, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 11942, Saudi Arabia
| | | | - Md Imtaiyaz Hassan
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India.
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7
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Bernardo-Faura M, Rinas M, Wirbel J, Pertsovskaya I, Pliaka V, Messinis DE, Vila G, Sakellaropoulos T, Faigle W, Stridh P, Behrens JR, Olsson T, Martin R, Paul F, Alexopoulos LG, Villoslada P, Saez-Rodriguez J. Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis. Genome Med 2021; 13:117. [PMID: 34271980 PMCID: PMC8284018 DOI: 10.1186/s13073-021-00925-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 06/14/2021] [Indexed: 11/21/2022] Open
Abstract
Background Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects. Methods Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies. Results Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Conclusions Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00925-8.
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Affiliation(s)
- Marti Bernardo-Faura
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.,Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Melanie Rinas
- Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany
| | - Jakob Wirbel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.,Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany
| | - Inna Pertsovskaya
- Institut d' Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain
| | - Vicky Pliaka
- School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece
| | | | - Gemma Vila
- Institut d' Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain
| | | | | | - Pernilla Stridh
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Janina R Behrens
- NeuroCure Clinical Research Center and Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Tomas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Friedemann Paul
- NeuroCure Clinical Research Center and Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Leonidas G Alexopoulos
- School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece. .,ProtATonce Ltd., Athens, Greece.
| | - Pablo Villoslada
- Institut d' Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Barcelona, Spain.
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK. .,Joint Research Center for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH-Aachen University, Aachen, Germany. .,Institute for Computational Biomedicine, Heidelberg University Hospital and Faculty of Medicine, Heidelberg University, Bioquant, Heidelberg, Germany.
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8
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Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia. J Pers Med 2021; 11:jpm11020117. [PMID: 33578936 PMCID: PMC7916657 DOI: 10.3390/jpm11020117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/05/2021] [Accepted: 02/05/2021] [Indexed: 12/12/2022] Open
Abstract
High throughput technologies such as deep sequencing and proteomics are increasingly becoming mainstream in clinical practice and support diagnosis and patient stratification. Developing computational models that recapitulate cell physiology and its perturbations in disease is a required step to help with the interpretation of results of high content experiments and to devise personalized treatments. As complete cell-models are difficult to achieve, given limited experimental information and insurmountable computational problems, approximate approaches should be considered. We present here a general approach to modeling complex diseases by embedding patient-specific genomics data into actionable logic models that take into account prior knowledge. We apply the strategy to acute myeloid leukemia (AML) and assemble a network of logical relationships linking most of the genes that are found frequently mutated in AML patients. We derive Boolean models from this network and we show that by priming the model with genomic data we can infer relevant patient-specific clinical features. Here we propose that the integration of literature-derived causal networks with patient-specific data should be explored to help bedside decisions.
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9
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Koukouli E, Wang D, Dondelinger F, Park J. A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response. PLoS Comput Biol 2021; 17:e1008066. [PMID: 33493149 PMCID: PMC7920352 DOI: 10.1371/journal.pcbi.1008066] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 03/01/2021] [Accepted: 12/17/2020] [Indexed: 11/18/2022] Open
Abstract
Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response.
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Affiliation(s)
- Evanthia Koukouli
- Department of Mathematics and Statistics, Fylde College, Lancaster University, Bailrigg, Lancaster, UK
| | - Dennis Wang
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Frank Dondelinger
- Centre for Health Informatics and Statistics, Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK
| | - Juhyun Park
- Department of Mathematics and Statistics, Fylde College, Lancaster University, Bailrigg, Lancaster, UK
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10
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Fundamental Boolean network modelling for childhood acute lymphoblastic leukaemia pathways. QUANTITATIVE BIOLOGY 2021. [DOI: 10.15302/j-qb-021-0280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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11
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Wang D, Hensman J, Kutkaite G, Toh TS, Galhoz A, Dry JR, Saez-Rodriguez J, Garnett MJ, Menden MP, Dondelinger F. A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates. eLife 2020; 9:e60352. [PMID: 33274713 PMCID: PMC7746236 DOI: 10.7554/elife.60352] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/04/2020] [Indexed: 12/16/2022] Open
Abstract
High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.
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Affiliation(s)
- Dennis Wang
- Sheffield Institute for Translational Neuroscience, University of SheffieldSheffieldUnited Kingdom
- Department of Computer Science, University of SheffieldSheffieldUnited Kingdom
| | | | - Ginte Kutkaite
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental HealthNeuherbergGermany
- Department of Biology, Ludwig-Maximilians University MunichMartinsriedGermany
| | - Tzen S Toh
- The Medical School, University of SheffieldSheffieldUnited Kingdom
| | - Ana Galhoz
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental HealthNeuherbergGermany
- Department of Biology, Ludwig-Maximilians University MunichMartinsriedGermany
| | - Jonathan R Dry
- Research and Early Development, Oncology R&D, AstraZenecaBostonUnited States
| | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine,Faculty of Medicine,Heidelberg Universityand Heidelberg University Hospital, BioquantHeidelbergGermany
| | | | - Michael P Menden
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental HealthNeuherbergGermany
- Department of Biology, Ludwig-Maximilians University MunichMartinsriedGermany
- German Center for Diabetes Research (DZD e.V.)NeuherbergGermany
| | - Frank Dondelinger
- Centre for Health Informatics, Computation and Statistics, Lancaster Medical School, Lancaster UniversityLancasterUnited Kingdom
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12
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Tsirvouli E, Touré V, Niederdorfer B, Vázquez M, Flobak Å, Kuiper M. A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines. Front Mol Biosci 2020; 7:502573. [PMID: 33195403 PMCID: PMC7581946 DOI: 10.3389/fmolb.2020.502573] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 09/22/2020] [Indexed: 11/23/2022] Open
Abstract
Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic.
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Affiliation(s)
- Eirini Tsirvouli
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Barbara Niederdorfer
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Miguel Vázquez
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- The Cancer Clinic, St. Olav’s University Hospital, Trondheim, Norway
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
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13
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Niederdorfer B, Touré V, Vazquez M, Thommesen L, Kuiper M, Lægreid A, Flobak Å. Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction. Front Physiol 2020; 11:862. [PMID: 32848834 PMCID: PMC7399174 DOI: 10.3389/fphys.2020.00862] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 06/26/2020] [Indexed: 12/16/2022] Open
Abstract
Discrete dynamical modeling shows promise in prioritizing drug combinations for screening efforts by reducing the experimental workload inherent to the vast numbers of possible drug combinations. We have investigated approaches to predict combination responses across different cancer cell lines using logic models generated from one generic prior-knowledge network representing 144 nodes covering major cancer signaling pathways. Cell-line specific models were configured to agree with baseline activity data from each unperturbed cell line. Testing against experimental data demonstrated a high number of true positive and true negative predictions, including also cell-specific responses. We demonstrate the possible enhancement of predictive capability of models by curation of literature knowledge further detailing subtle biologically founded signaling mechanisms in the model topology. In silico model analysis pinpointed a subset of network nodes highly influencing model predictions. Our results indicate that the performance of logic models can be improved by focusing on high-influence node protein activity data for model configuration and that these nodes accommodate high information flow in the regulatory network.
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Affiliation(s)
- Barbara Niederdorfer
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Miguel Vazquez
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.,Barcelona Supercomputing Center, Barcelona, Spain
| | - Liv Thommesen
- Department of Biomedical Laboratory Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Astrid Lægreid
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.,The Cancer Clinic, St. Olav's University Hospital, Trondheim, Norway
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14
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Lim JT, Singh N, Leuvano LA, Calvert VS, Petricoin EF, Teachey DT, Lock RB, Padi M, Kraft AS, Padi SKR. PIM Kinase Inhibitors Block the Growth of Primary T-cell Acute Lymphoblastic Leukemia: Resistance Pathways Identified by Network Modeling Analysis. Mol Cancer Ther 2020; 19:1809-1821. [PMID: 32753387 DOI: 10.1158/1535-7163.mct-20-0160] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/27/2020] [Accepted: 07/10/2020] [Indexed: 11/16/2022]
Abstract
Despite significant progress in understanding the genetic landscape of T-cell acute lymphoblastic leukemia (T-ALL), the discovery of novel therapeutic targets has been difficult. Our results demonstrate that the levels of PIM1 protein kinase is elevated in early T-cell precursor ALL (ETP-ALL) but not in mature T-ALL primary samples. Small-molecule PIM inhibitor (PIMi) treatment decreases leukemia burden in ETP-ALL. However, treatment of animals carrying ETP-ALL with PIMi was not curative. To model other pathways that could be targeted to complement PIMi activity, HSB-2 cells, previously characterized as a PIMi-sensitive T-ALL cell line, were grown in increasing doses of PIMi. Gene set enrichment analysis of RNA sequencing data and functional enrichment of network modules demonstrated that the HOXA9, mTOR, MYC, NFκB, and PI3K-AKT pathways were activated in HSB-2 cells after long-term PIM inhibition. Reverse phase protein array-based pathway activation mapping demonstrated alterations in the mTOR, PI3K-AKT, and NFκB pathways, as well. PIMi-tolerant HSB-2 cells contained phosphorylated RelA-S536 consistent with activation of the NFκB pathway. The combination of NFκB and PIMis markedly reduced the proliferation in PIMi-resistant leukemic cells showing that this pathway plays an important role in driving the growth of T-ALL. Together these results demonstrate key pathways that are activated when HSB-2 cell line develop resistance to PIMi and suggest pathways that can be rationally targeted in combination with PIM kinases to inhibit T-ALL growth.
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Affiliation(s)
- James T Lim
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona
| | - Neha Singh
- University of Arizona Cancer Center, University of Arizona, Tucson, Arizona
| | - Libia A Leuvano
- University of Arizona Cancer Center, University of Arizona, Tucson, Arizona
| | - Valerie S Calvert
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, Virginia
| | - Emanuel F Petricoin
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, Virginia
| | - David T Teachey
- Division of Oncology, Department of Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Richard B Lock
- Children's Cancer Institute, School of Women's and Children's Health, UNSW Sydney, Sydney, Australia
| | - Megha Padi
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, Arizona
- Bioinformatics Shared Resource, University of Arizona Cancer Center, Tucson, Arizona
| | - Andrew S Kraft
- University of Arizona Cancer Center, University of Arizona, Tucson, Arizona.
| | - Sathish K R Padi
- University of Arizona Cancer Center, University of Arizona, Tucson, Arizona.
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15
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Abstract
Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.
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Affiliation(s)
- Matthew A Clarke
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Jasmin Fisher
- Department of Biochemistry, University of Cambridge, Cambridge, UK.
- UCL Cancer Institute, University College London, London, UK.
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16
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Hall BA, Fisher J. Constructing and Analyzing Computational Models of Cell Signaling with BioModelAnalyzer. CURRENT PROTOCOLS IN BIOINFORMATICS 2020; 69:e95. [PMID: 32078258 DOI: 10.1002/cpbi.95] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BioModelAnalyzer (BMA) is an open-source graphical tool for the development of executable models of protein and gene networks within cells. Based upon the Qualitative Networks formalism, the user can rapidly construct large networks, either manually or by connecting motifs selected from a built-in library. After the appropriate functions for each variable are defined, the user has access to three analysis engines to test the model. In addition to standard simulation tools, BMA includes an interface to the stability-testing algorithm and to a graphical Linear Temporal Logic (LTL) editor and analysis tool. Alongside this, we have developed a novel ChatBot to aid users constructing LTL queries and to explain the interface and run through tutorials. Here we present worked examples of model construction and testing via the interface. As an initial example, we discuss fate decisions in Dictyostelium discoidum and cAMP signaling. We go on to describe the workflow leading to the construction of a published model of the germline of C. elegans. Finally, we demonstrate how to construct simple models from the built-in network motif library. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Modeling the signaling network of Dictyostelium discoidum Basic Protocol 2: Modeling the germline progression of Caenorhabditis elegans Basic Protocol 3: Constructing a model of the cell cycle using motifs.
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Affiliation(s)
- Benjamin A Hall
- MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom
| | - Jasmin Fisher
- UCL Cancer Institute, University College London, London, United Kingdom
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17
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Abstract
Dynamic mechanistic models, that is, those that can simulate behavior over time courses, are a cornerstone of molecular systems biology. They are being used to model molecular mechanisms with varying degrees of granularity-from elementary reactions to causal links-and to describe these systems by various dynamic mathematical frameworks, such as Boolean networks or systems of differential equations. The models can be based exclusively on experimental data, or on prior knowledge of the underlying biological processes. The latter are typically generic, but can be adapted to a certain context, such as a particular cell type, after training with context-specific data. Dynamic mechanistic models that are based on biological knowledge have great potential for modeling specific systems, because they require less data for training to provide biological insight in particular into causal mechanisms, and to extrapolate to scenarios that are outside the conditions they have been trained on.
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Affiliation(s)
- Julio Saez‐Rodriguez
- Faculty of MedicineHeidelberg University and Heidelberg University HospitalInstitute for Computational BiomedicineBioquantHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
| | - Nils Blüthgen
- Institute of PathologyCharité ‐ Universitätsmedizin BerlinBerlinGermany
- IRI Life SciencesHumboldt Universität zu BerlinBerlinGermany
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18
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Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019. [DOI: 78495111110.1038/s41573-019-0050-3' target='_blank'>'"<>78495111110.1038/s41573-019-0050-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [78495111110.1038/s41573-019-0050-3','', '10.1158/0008-5472.can-16-1578')">Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
78495111110.1038/s41573-019-0050-3" />
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19
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Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2019; 19:353-364. [DOI: 10.1038/s41573-019-0050-3] [Citation(s) in RCA: 222] [Impact Index Per Article: 44.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/28/2019] [Indexed: 12/17/2022]
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20
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Keshava N, Toh TS, Yuan H, Yang B, Menden MP, Wang D. Defining subpopulations of differential drug response to reveal novel target populations. NPJ Syst Biol Appl 2019; 5:36. [PMID: 31602313 PMCID: PMC6776548 DOI: 10.1038/s41540-019-0113-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 09/05/2019] [Indexed: 02/08/2023] Open
Abstract
Personalised medicine has predominantly focused on genetically altered cancer genes that stratify drug responses, but there is a need to objectively evaluate differential pharmacology patterns at a subpopulation level. Here, we introduce an approach based on unsupervised machine learning to compare the pharmacological response relationships between 327 pairs of cancer therapies. This approach integrated multiple measures of response to identify subpopulations that react differently to inhibitors of the same or different targets to understand mechanisms of resistance and pathway cross-talk. MEK, BRAF, and PI3K inhibitors were shown to be effective as combination therapies for particular BRAF mutant subpopulations. A systematic analysis of preclinical data for a failed phase III trial of selumetinib combined with docetaxel in lung cancer suggests potential indications in pancreatic and colorectal cancers with KRAS mutation. This data-informed study exemplifies a method for stratified medicine to identify novel cancer subpopulations, their genetic biomarkers, and effective drug combinations.
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Affiliation(s)
| | - Tzen S. Toh
- The Medical School, University of Sheffield, Sheffield, S10 2RX UK
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ UK
| | - Haobin Yuan
- Department of Computer Science, University of Sheffield, Sheffield, S1 4DP UK
| | - Bingxun Yang
- Department of Computer Science, University of Sheffield, Sheffield, S1 4DP UK
| | - Michael P. Menden
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Department of Biology, Ludwig-Maximilians University Munich, 82152 Martinsried, Germany
- German Centre for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany
| | - Dennis Wang
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ UK
- Department of Computer Science, University of Sheffield, Sheffield, S1 4DP UK
- NIHR Sheffield Biomedical Research Centre, Sheffield, S10 2HQ UK
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21
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Silverbush D, Sharan R. A systematic approach to orient the human protein-protein interaction network. Nat Commun 2019; 10:3015. [PMID: 31289271 PMCID: PMC6617457 DOI: 10.1038/s41467-019-10887-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Accepted: 06/06/2019] [Indexed: 11/16/2022] Open
Abstract
The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.
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Affiliation(s)
- Dana Silverbush
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Roded Sharan
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel.
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22
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Menden MP, Wang D, Mason MJ, Szalai B, Bulusu KC, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang IS, Ghazoui Z, Ahsen ME, Vogel R, Neto EC, Norman T, Tang EKY, Garnett MJ, Veroli GYD, Fawell S, Stolovitzky G, Guinney J, Dry JR, Saez-Rodriguez J. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nat Commun 2019; 10:2674. [PMID: 31209238 PMCID: PMC6572829 DOI: 10.1038/s41467-019-09799-2] [Citation(s) in RCA: 166] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 04/01/2019] [Indexed: 02/06/2023] Open
Abstract
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
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Affiliation(s)
- Michael P Menden
- Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, SG8 6EH, UK
- European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Munich, D-85764, Germany
| | - Dennis Wang
- Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, SG8 6EH, UK
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2TN, UK
| | | | - Bence Szalai
- Department of Physiology, Faculty of Medicine, Semmelweis University, Budapest, 1085, Hungary
- Laboratory of Molecular Physiology, Hungarian Academy of Sciences and Semmelweis University (MTA-SE), Budapest, 1085, Hungary
- RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine, Aachen, 52062, Germany
| | - Krishna C Bulusu
- Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, SG8 6EH, UK
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, USA
| | - Thomas Yu
- Sage Bionetworks, Seattle, WA, 98121, USA
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, 02841, Korea
| | - Minji Jeon
- Department of Computer Science and Engineering, Korea University, Seoul, 02841, Korea
| | | | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, USA
| | - Mikhail Zaslavskiy
- Independent Consultant in Computational Biology, Owkin, Inc., New York, NY, 10022, USA
| | | | - Zara Ghazoui
- Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, SG8 6EH, UK
| | - Mehmet Eren Ahsen
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York, 10598, USA
| | - Robert Vogel
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York, 10598, USA
| | | | | | - Eric K Y Tang
- Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, SG8 6EH, UK
| | | | - Giovanni Y Di Veroli
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Cambridge, SG8 6EH, UK
| | - Stephen Fawell
- Oncology, IMED Biotech Unit, AstraZeneca, R&D Boston, Waltham, MA, 02451, USA
| | - Gustavo Stolovitzky
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York, 10598, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, USA
| | | | - Jonathan R Dry
- Oncology, IMED Biotech Unit, AstraZeneca, R&D Boston, Waltham, MA, 02451, USA.
| | - Julio Saez-Rodriguez
- European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK.
- RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine, Aachen, 52062, Germany.
- Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant, 69120, Heidelberg, Germany.
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23
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Kalamara A, Tobalina L, Saez-Rodriguez J. How to find the right drug for each patient? Advances and challenges in pharmacogenomics. CURRENT OPINION IN SYSTEMS BIOLOGY 2018; 10:53-62. [PMID: 31763498 PMCID: PMC6855262 DOI: 10.1016/j.coisb.2018.07.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Cancer is a highly heterogeneous disease with complex underlying biology. For these reasons, effective cancer treatment is still a challenge. Nowadays, it is clear that a cancer therapy that fits all the cases cannot be found, and as a result the design of therapies tailored to the patient's molecular characteristics is needed. Pharmacogenomics aims to study the relationship between an individual's genotype and drug response. Scientists use different biological models, ranging from cell lines to mouse models, as proxies for patients for preclinical and translational studies. The rapid development of "-omics" technologies is increasing the amount of features that can be measured in these models, expanding the possibilities of finding predictive biomarkers of drug response. Finding these relationships requires diverse computational approaches ranging from machine learning to dynamic modeling. Despite major advances, we are still far from being able to precisely predict drug efficacy in cancer models, let alone directly on patients. We believe that the new experimental techniques and computational approaches covered in this review will bring us closer to this goal.
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Affiliation(s)
- Angeliki Kalamara
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
| | - Luis Tobalina
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
| | - Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
- Heidelberg University, Faculty of Medicine, Institute of Computational Biomedicine, Heidelberg, Germany
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24
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Buetti-Dinh A, Friedman R. Computer simulations of the signalling network in FLT3 +-acute myeloid leukaemia - indications for an optimal dosage of inhibitors against FLT3 and CDK6. BMC Bioinformatics 2018; 19:155. [PMID: 29699481 PMCID: PMC5921566 DOI: 10.1186/s12859-018-2145-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 04/03/2018] [Indexed: 12/31/2022] Open
Abstract
Background Mutations in the FMS-like tyrosine kinase 3 (FLT3) are associated with uncontrolled cellular functions that contribute to the development of acute myeloid leukaemia (AML). We performed computer simulations of the FLT3-dependent signalling network in order to study the pathways that are involved in AML development and resistance to targeted therapies. Results Analysis of the simulations revealed the presence of alternative pathways through phosphoinositide 3 kinase (PI3K) and SH2-containing sequence proteins (SHC), that could overcome inhibition of FLT3. Inhibition of cyclin dependent kinase 6 (CDK6), a related molecular target, was also tested in the simulation but was not found to yield sufficient benefits alone. Conclusions The PI3K pathway provided a basis for resistance to treatments. Alternative signalling pathways could not, however, restore cancer growth signals (proliferation and loss of apoptosis) to the same levels as prior to treatment, which may explain why FLT3 resistance mutations are the most common resistance mechanism. Finally, sensitivity analysis suggested the existence of optimal doses of FLT3 and CDK6 inhibitors in terms of efficacy and toxicity. Electronic supplementary material The online version of this article (10.1186/s12859-018-2145-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Antoine Buetti-Dinh
- Department of Chemistry and Biomedical Sciences, Linnæus University, Norra vägen 49, Kalmar, SE-391 82, Sweden.,Linnæus University Centre for Biomaterials Chemistry, Linnæus University, Norra vägen 49, Kalmar, SE-391 82, Sweden.,Centre for Ecology and Evolution in Microbial Model Systems, Linnæus University, Landgången 3, Kalmar, SE-391 82, Sweden.,Institute of Computational Science, Faculty of Informatics, Università della Svizzera Italiana, Via Giuseppe Buffi 13, Lugano, CH-6900, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, Lausanne, CH-1015, Switzerland
| | - Ran Friedman
- Department of Chemistry and Biomedical Sciences, Linnæus University, Norra vägen 49, Kalmar, SE-391 82, Sweden. .,Linnæus University Centre for Biomaterials Chemistry, Linnæus University, Norra vägen 49, Kalmar, SE-391 82, Sweden.
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25
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Paterson YZ, Shorthouse D, Pleijzier MW, Piterman N, Bendtsen C, Hall BA, Fisher J. A toolbox for discrete modelling of cell signalling dynamics. Integr Biol (Camb) 2018; 10:370-382. [DOI: 10.1039/c8ib00026c] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
We present a library of network motifs for the development of complex and realistic biological network models using the BioModelAnalyzer, and demonstrate their wider value by using them to construct a model of the cell cycle.
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Affiliation(s)
| | | | | | - Nir Piterman
- Department of Informatics
- University of Leicester
- Leicester
- UK
| | - Claus Bendtsen
- Quantitative Biology
- Discovery Sciences
- IMED Biotech Unit
- AstraZeneca
- Cambridge
| | | | - Jasmin Fisher
- Department of Biochemistry
- University of Cambridge
- Cambridge
- UK
- Microsoft Research
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26
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Traynard P, Tobalina L, Eduati F, Calzone L, Saez-Rodriguez J. Logic Modeling in Quantitative Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:499-511. [PMID: 28681552 PMCID: PMC5572374 DOI: 10.1002/psp4.12225] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/01/2017] [Accepted: 06/15/2017] [Indexed: 12/12/2022]
Abstract
Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).
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Affiliation(s)
- Pauline Traynard
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Luis Tobalina
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
| | - Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
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27
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Eduati F, Doldàn-Martelli V, Klinger B, Cokelaer T, Sieber A, Kogera F, Dorel M, Garnett MJ, Blüthgen N, Saez-Rodriguez J. Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type-Specific Dynamic Logic Models. Cancer Res 2017; 77:3364-3375. [PMID: 28381545 DOI: 10.1158/0008-5472.can-17-0078] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Revised: 03/17/2017] [Accepted: 03/31/2017] [Indexed: 12/20/2022]
Abstract
Genomic features are used as biomarkers of sensitivity to kinase inhibitors used widely to treat human cancer, but effective patient stratification based on these principles remains limited in impact. Insofar as kinase inhibitors interfere with signaling dynamics, and, in turn, signaling dynamics affects inhibitor responses, we investigated associations in this study between cell-specific dynamic signaling pathways and drug sensitivity. Specifically, we measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines, building cell line-specific dynamic logic models of underlying signaling networks. Model parameters representing pathway dynamics were used as features to predict sensitivity to a panel of 27 drugs. Specific parameters of signaling dynamics correlated strongly with drug sensitivity for 14 of the drugs, 9 of which had no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by coblockade of GSK3, which was not found based on associations with genomic data. These results suggest that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes. Cancer Res; 77(12); 3364-75. ©2017 AACR.
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Affiliation(s)
- Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Victoria Doldàn-Martelli
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom.,Departamento de Física de la Materia Condensada, Condensed Matter Physics Center (IFIMAC) and Instituto Nicolás Cabrera, Facultad de Ciencias, Universidad Autónoma de Madrid, Madrid, Spain
| | - Bertram Klinger
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Thomas Cokelaer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Anja Sieber
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Fiona Kogera
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Mathurin Dorel
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Mathew J Garnett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany. .,Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom. .,Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, Aachen, Germany
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