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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [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: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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Bartelink IH, Jones EF, Shahidi‐Latham SK, Lee PRE, Zheng Y, Vicini P, van ‘t Veer L, Wolf D, Iagaru A, Kroetz DL, Prideaux B, Cilliers C, Thurber GM, Wimana Z, Gebhart G. Tumor Drug Penetration Measurements Could Be the Neglected Piece of the Personalized Cancer Treatment Puzzle. Clin Pharmacol Ther 2019; 106:148-163. [PMID: 30107040 PMCID: PMC6617978 DOI: 10.1002/cpt.1211] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 07/30/2018] [Indexed: 12/30/2022]
Abstract
Precision medicine aims to use patient genomic, epigenomic, specific drug dose, and other data to define disease patterns that may potentially lead to an improved treatment outcome. Personalized dosing regimens based on tumor drug penetration can play a critical role in this approach. State-of-the-art techniques to measure tumor drug penetration focus on systemic exposure, tissue penetration, cellular or molecular engagement, and expression of pharmacological activity. Using in silico methods, this information can be integrated to bridge the gap between the therapeutic regimen and the pharmacological link with clinical outcome. These methodologies are described, and challenges ahead are discussed. Supported by many examples, this review shows how the combination of these techniques provides enhanced patient-specific information on drug accessibility at the tumor tissue level, target binding, and downstream pharmacology. Our vision of how to apply tumor drug penetration measurements offers a roadmap for the clinical implementation of precision dosing.
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Affiliation(s)
- Imke H. Bartelink
- Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Clinical Pharmacology, Pharmacometrics and DMPK (CPD)MedImmuneSouth San FranciscoCaliforniaUSA
- Department of Clinical Pharmacology and PharmacyAmsterdam UMCVrije Universiteit AmsterdamThe Netherlands
| | - Ella F. Jones
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | | | - Pei Rong Evelyn Lee
- Department of Laboratory Medicine of the UCSF Helen Diller Family Comprehensive Cancer CenterUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Yanan Zheng
- Clinical Pharmacology, Pharmacometrics and DMPK (CPD)MedImmuneSouth San FranciscoCaliforniaUSA
| | - Paolo Vicini
- Clinical Pharmacology, Pharmacometrics and DMPK (CPD)MedImmuneCambridgeUK
| | - Laura van ‘t Veer
- Department of Laboratory Medicine of the UCSF Helen Diller Family Comprehensive Cancer CenterUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Denise Wolf
- Department of Laboratory Medicine of the UCSF Helen Diller Family Comprehensive Cancer CenterUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Andrei Iagaru
- Division of Nuclear Medicine and Molecular Imaging at Stanford Health CareStanfordCaliforniaUSA
| | - Deanna L. Kroetz
- Department of Bioengineering and Therapeutic Sciences (BTS)School of PharmacyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Brendan Prideaux
- Rutgers New Jersey Medical SchoolPublic Health Research InstituteRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Cornelius Cilliers
- Departments of Chemical Engineering and Biomedical EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - Greg M. Thurber
- Departments of Chemical Engineering and Biomedical EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - Zena Wimana
- Institut Jules BordetUniversité Libre de Bruxelles (ULB)BrusselsBelgium
| | - Geraldine Gebhart
- Institut Jules BordetUniversité Libre de Bruxelles (ULB)BrusselsBelgium
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Jordan EJ, Patil K, Suresh K, Park JH, Mosse YP, Lemmon MA, Radhakrishnan R. Computational algorithms for in silico profiling of activating mutations in cancer. Cell Mol Life Sci 2019; 76:2663-2679. [PMID: 30982079 PMCID: PMC6589134 DOI: 10.1007/s00018-019-03097-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 04/01/2019] [Accepted: 04/08/2019] [Indexed: 12/17/2022]
Abstract
Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.
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Affiliation(s)
- E Joseph Jordan
- Graduate Group in Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA, USA
| | - Keshav Patil
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Krishna Suresh
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Jin H Park
- Department of Pharmacology, Yale University, New Haven, CT, USA
- Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Yael P Mosse
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mark A Lemmon
- Department of Pharmacology, Yale University, New Haven, CT, USA
- Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Ravi Radhakrishnan
- Graduate Group in Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
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Pharmacodynamic Therapeutic Drug Monitoring for Cancer: Challenges, Advances, and Future Opportunities. Ther Drug Monit 2019; 41:142-159. [DOI: 10.1097/ftd.0000000000000606] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Ouzounoglou E, Kolokotroni E, Stanulla M, Stamatakos GS. A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator. Interface Focus 2018; 8:20160163. [PMID: 29285342 PMCID: PMC5740218 DOI: 10.1098/rsfs.2016.0163] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.
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Affiliation(s)
- Eleftherios Ouzounoglou
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Martin Stanulla
- Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany
| | - Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
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Stamatakos GS, Giatili SG. A Numerical Handling of the Boundary Conditions Imposed by the Skull on an Inhomogeneous Diffusion-Reaction Model of Glioblastoma Invasion Into the Brain: Clinical Validation Aspects. Cancer Inform 2017; 16:1176935116684824. [PMID: 28469383 PMCID: PMC5392020 DOI: 10.1177/1176935116684824] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Accepted: 11/24/2016] [Indexed: 12/22/2022] Open
Abstract
A novel explicit triscale reaction-diffusion numerical model of glioblastoma multiforme tumor growth is presented. The model incorporates the handling of Neumann boundary conditions imposed by the cranium and takes into account both the inhomogeneous nature of human brain and the complexity of the skull geometry. The finite-difference time-domain method is adopted. To demonstrate the workflow of a possible clinical validation procedure, a clinical case/scenario is addressed. A good agreement of the in silico calculated value of the doubling time (ie, the time for tumor volume to double) with the value of the same quantity based on tomographic imaging data has been observed. A theoretical exploration suggests that a rough but still quite informative value of the doubling time may be calculated based on a homogeneous brain model. The model could serve as the main component of a continuous mathematics-based glioblastoma oncosimulator aiming at supporting the clinician in the optimal patient-individualized design of treatment using the patient's multiscale data and experimenting in silico (ie, on the computer).
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Affiliation(s)
- Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografou, Greece
| | - Stavroula G Giatili
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografou, Greece
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Christodoulou NA, Tousert NE, Georgiadi EC, Argyri KD, Misichroni FD, Stamatakos GS. A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine. Cancer Inform 2016; 15:219-235. [PMID: 27812280 PMCID: PMC5084707 DOI: 10.4137/cin.s40189] [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: 05/24/2016] [Revised: 09/04/2016] [Accepted: 09/09/2016] [Indexed: 11/05/2022] Open
Abstract
The plethora of available disease prediction models and the ongoing process of their application into clinical practice - following their clinical validation - have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work. In addition, the end users should be provided with all the necessary interfaces for model execution and effortless result retrieval. We therefore propose a software infrastructure, based on a tool, model and data repository that handles the storage of models and pertinent execution-related data, along with functionalities for execution management, communication with third-party applications, user-friendly interfaces to access and use the infrastructure with minimal effort and basic security features.
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Affiliation(s)
- Nikolaos A. Christodoulou
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografos, Greece
| | - Nikolaos E. Tousert
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografos, Greece
| | - Eleni Ch. Georgiadi
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografos, Greece
| | - Katerina D. Argyri
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografos, Greece
| | - Fay D. Misichroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografos, Greece
| | - Georgios S. Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografos, Greece
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Bucur A, van Leeuwen J, Christodoulou N, Sigdel K, Argyri K, Koumakis L, Graf N, Stamatakos G. Workflow-driven clinical decision support for personalized oncology. BMC Med Inform Decis Mak 2016; 16 Suppl 2:87. [PMID: 27460182 PMCID: PMC4965727 DOI: 10.1186/s12911-016-0314-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND The adoption in oncology of Clinical Decision Support (CDS) may help clinical users to efficiently deal with the high complexity of the domain, lead to improved patient outcomes, and reduce the current knowledge gap between clinical research and practice. While significant effort has been invested in the implementation of CDS, the uptake in the clinic has been limited. The barriers to adoption have been extensively discussed in the literature. In oncology, current CDS solutions are not able to support the complex decisions required for stratification and personalized treatment of patients and to keep up with the high rate of change in therapeutic options and knowledge. RESULTS To address these challenges, we propose a framework enabling efficient implementation of meaningful CDS that incorporates a large variety of clinical knowledge models to bring to the clinic comprehensive solutions leveraging the latest domain knowledge. We use both literature-based models and models built within the p-medicine project using the rich datasets from clinical trials and care provided by the clinical partners. The framework is open to the biomedical community, enabling reuse of deployed models by third-party CDS implementations and supporting collaboration among modelers, CDS implementers, biomedical researchers and clinicians. To increase adoption and cope with the complexity of patient management in oncology, we also support and leverage the clinical processes adhered to by healthcare organizations. We design an architecture that extends the CDS framework with workflow functionality. The clinical models are embedded in the workflow models and executed at the right time, when and where the recommendations are needed in the clinical process. CONCLUSIONS In this paper we present our CDS framework developed in p-medicine and the CDS implementation leveraging the framework. To support complex decisions, the framework relies on clinical models that encapsulate relevant clinical knowledge. Next to assisting the decisions, this solution supports by default (through modeling and implementation of workflows) the decision processes as well and exploits the knowledge embedded in those processes.
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Affiliation(s)
- Anca Bucur
- Precision and Decentralized Diagnostics, Philips Research, Eindhoven, The Netherlands.
| | - Jasper van Leeuwen
- Precision and Decentralized Diagnostics, Philips Research, Eindhoven, The Netherlands
| | | | - Kamana Sigdel
- Precision and Decentralized Diagnostics, Philips Research, Eindhoven, The Netherlands
| | - Katerina Argyri
- National Technical University of Athens, ICCS, Athens, Greece
| | | | - Norbert Graf
- Department of Pediatric Oncology and Hematology, Saarland University, Homburg, Germany
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Argyri KD, Dionysiou DD, Misichroni FD, Stamatakos GS. Numerical simulation of vascular tumour growth under antiangiogenic treatment: addressing the paradigm of single-agent bevacizumab therapy with the use of experimental data. Biol Direct 2016; 11:12. [PMID: 27005569 PMCID: PMC4804544 DOI: 10.1186/s13062-016-0114-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2016] [Accepted: 03/14/2016] [Indexed: 11/30/2022] Open
Abstract
Background Antiangiogenic agents have been recently added to the oncological armamentarium with bevacizumab probably being the most popular representative in current clinical practice. The elucidation of the mode of action of these agents is a prerequisite for personalized prediction of antiangiogenic treatment response and selection of patients who may benefit from this kind of therapy. To this end, having used as a basis a preexisting continuous vascular tumour growth model which addresses the targeted nature of antiangiogenic treatment, we present a paper characterized by the following three features. First, the integration of a two-compartmental bevacizumab specific pharmacokinetic module into the core of the aforementioned preexisting model. Second, its mathematical modification in order to reproduce the asymptotic behaviour of tumour volume in the theoretical case of a total destruction of tumour neovasculature. Third, the exploitation of a range of published animal datasets pertaining to antitumour efficacy of bevacizumab on various tumour types (breast, lung, head and neck, colon). Results Results for both the unperturbed growth and the treatment module reveal qualitative similarities with experimental observations establishing the biologically acceptable behaviour of the model. The dynamics of the untreated tumour has been studied via a parameter analysis, revealing the role of each relevant input parameter to tumour evolution. The combined effect of endogenous proangiogenic and antiangiogenic factors on the angiogenic potential of a tumour is also studied, in order to capture the dynamics of molecular competition between the two key-players of tumoural angiogenesis. The adopted methodology also allows accounting for the newly recognized direct antitumour effect of the specific agent. Conclusions Interesting observations have been made, suggesting a potential size-dependent tumour response to different treatment modalities and determining the relative timing of cytotoxic versus antiangiogenic agents administration. Insight into the comparative effectiveness of different antiangiogenic treatment strategies is revealed. The results of a series of in vivo experiments in mice bearing diverse types of tumours (breast, lung, head and neck, colon) and treated with bevacizumab are successfully reproduced, supporting thus the validity of the underlying model. Reviewers This article was reviewed by L. Hanin, T. Radivoyevitch and L. Edler.
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Affiliation(s)
- Katerina D Argyri
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografos, GR 157 80, Athens, Greece
| | - Dimitra D Dionysiou
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografos, GR 157 80, Athens, Greece
| | - Fay D Misichroni
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografos, GR 157 80, Athens, Greece
| | - Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografos, GR 157 80, Athens, Greece.
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Ouzounoglou E, Dionysiou D, Stamatakos GS. Differentiation resistance through altered retinoblastoma protein function in acute lymphoblastic leukemia: in silico modeling of the deregulations in the G1/S restriction point pathway. BMC SYSTEMS BIOLOGY 2016; 10:23. [PMID: 26932523 PMCID: PMC4774111 DOI: 10.1186/s12918-016-0264-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 01/31/2016] [Indexed: 12/13/2022]
Abstract
BACKGROUND As in many cancer types, the G1/S restriction point (RP) is deregulated in Acute Lymphoblastic Leukemia (ALL). Hyper-phosphorylated retinoblastoma protein (hyper-pRb) is found in high levels in ALL cells. Nevertheless, the ALL lymphocyte proliferation rate for the average patient is surprisingly low compared to its normal counterpart of the same maturation level. Additionally, as stated in literature, ALL cells possibly reside at or beyond the RP which is located in the late-G1 phase. This state may favor their differentiation resistant phenotype. A major phenomenon contributing to this fact is thought to be the observed limited redundancy in the phosphorylation of retinoblastoma protein (pRb) by the various Cyclin Dependent Kinases (Cdks). The latter may result in partial loss of pRb functions despite hyper-phosphorylation. RESULTS To test this hypothesis, an in silico model aiming at simulating the biochemical regulation of the RP in ALL is introduced. By exploiting experimental findings derived from leukemic cells and following a semi-quantitative calibration procedure, the model has been shown to satisfactorily reproduce such a behavior for the RP pathway. At the same time, the calibrated model has been proved to be in agreement with the observed variation in the ALL cell cycle duration. CONCLUSIONS The proposed model aims to contribute to a better understanding of the complex phenomena governing the leukemic cell cycle. At the same time it constitutes a significant first step in the creation of a personalized proliferation rate predictor that can be used in the context of multiscale cancer modeling. Such an approach is expected to play an important role in the refinement and the advancement of mechanistic modeling of ALL in the context of the emergent and promising scientific domains of In Silico Oncology and more generally In Silico Medicine.
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Affiliation(s)
- Eleftherios Ouzounoglou
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografou, 15780, Athens, Greece.
| | - Dimitra Dionysiou
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografou, 15780, Athens, Greece.
| | - Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografou, 15780, Athens, Greece.
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Johnson D, Osborne J, Wang Z, Marias K. Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes. Cancer Inform 2016; 14:105-8. [PMID: 26798209 PMCID: PMC4711392 DOI: 10.4137/cin.s37982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Affiliation(s)
- David Johnson
- Senior Research Associate, Oxford e-Research Centre, University of Oxford, Oxford, UK
| | - James Osborne
- Lecturer in Applied Mathematics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Zhihui Wang
- Associate Professor, Department of NanoMedicine and BioMedical Engineering, University of Texas Medical School at Houston, Houston, TX, USA
| | - Kostas Marias
- Principal Researcher and Head, Computational Biomedicine Laboratory, Institute of Computer Science of the Foundation for Research and Technology Hellas (ICS-FORTH), Heraklion, Greece
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Li XL, Oduola WO, Qian L, Dougherty ER. Integrating Multiscale Modeling with Drug Effects for Cancer Treatment. Cancer Inform 2016; 14:21-31. [PMID: 26792977 PMCID: PMC4712979 DOI: 10.4137/cin.s30797] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Revised: 11/08/2015] [Accepted: 11/15/2015] [Indexed: 12/12/2022] Open
Abstract
In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.
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Affiliation(s)
- Xiangfang L. Li
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Wasiu O. Oduola
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Lijun Qian
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Edward R. Dougherty
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
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Barbolosi D, Ciccolini J, Lacarelle B, Barlési F, André N. Computational oncology — mathematical modelling of drug regimens for precision medicine. Nat Rev Clin Oncol 2015; 13:242-54. [DOI: 10.1038/nrclinonc.2015.204] [Citation(s) in RCA: 144] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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Sfakianaki P, Koumakis L, Sfakianakis S, Iatraki G, Zacharioudakis G, Graf N, Marias K, Tsiknakis M. Semantic biomedical resource discovery: a Natural Language Processing framework. BMC Med Inform Decis Mak 2015; 15:77. [PMID: 26423616 PMCID: PMC4591066 DOI: 10.1186/s12911-015-0200-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 09/21/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. METHODS A Natural Language Processing engine which can "translate" free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ). RESULTS For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. CONCLUSIONS There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.
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Affiliation(s)
- Pepi Sfakianaki
- Foundation for Research and Technology Hellas (FORTH), Institute of Computer Science, N. Plastira 100, Vassilika Vouton, Heraklion, Crete Greece
| | - Lefteris Koumakis
- Foundation for Research and Technology Hellas (FORTH), Institute of Computer Science, N. Plastira 100, Vassilika Vouton, Heraklion, Crete Greece
| | - Stelios Sfakianakis
- Foundation for Research and Technology Hellas (FORTH), Institute of Computer Science, N. Plastira 100, Vassilika Vouton, Heraklion, Crete Greece
| | - Galatia Iatraki
- Foundation for Research and Technology Hellas (FORTH), Institute of Computer Science, N. Plastira 100, Vassilika Vouton, Heraklion, Crete Greece
| | - Giorgos Zacharioudakis
- Foundation for Research and Technology Hellas (FORTH), Institute of Computer Science, N. Plastira 100, Vassilika Vouton, Heraklion, Crete Greece
| | - Norbert Graf
- Paediatric Haematology and Oncology, Saarland University Hospital, Homburg, Germany
| | - Kostas Marias
- Foundation for Research and Technology Hellas (FORTH), Institute of Computer Science, N. Plastira 100, Vassilika Vouton, Heraklion, Crete Greece
| | - Manolis Tsiknakis
- Foundation for Research and Technology Hellas (FORTH), Institute of Computer Science, N. Plastira 100, Vassilika Vouton, Heraklion, Crete Greece
- Department of Informatics Engineering, Technological Educational Institute, Heraklion, Crete Greece
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15
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Belfatto A, Riboldi M, Ciardo D, Cecconi A, Lazzari R, Jereczek-Fossa BA, Orecchia R, Baroni G, Cerveri P. Adaptive Mathematical Model of Tumor Response to Radiotherapy Based on CBCT Data. IEEE J Biomed Health Inform 2015; 20:802-809. [PMID: 26173223 DOI: 10.1109/jbhi.2015.2453437] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mathematical modeling of tumor response to radiotherapy has the potential of enhancing the quality of the treatment plan, which can be even tailored on an individual basis. Lack of extensive in vivo validation has prevented, however, reliable clinical translation of modeling outcomes. Image-guided radiotherapy is a consolidated treatment modality based on computed tomographic (CT) imaging for tumor delineation and volumetric cone beam CT data for periodic checks during treatment. In this study, a macroscopic model of tumor growth and radiation response is proposed, being able to adapt along the treatment course as volumetric tumor data become available. Model parameter learning was based on cone beam CT images in 13 uterine cervical cancer patients, subdivided into three groups (G1, G2, G3) according to tumor type and treatment. Three group-specific parameter sets (PS1, PS2, and PS3) on one general parameter set (PSa) were applied. The corresponding average model fitting errors were 14%, 18%, 13%, and 21%, respectively. The model adaptation testing was performed using volume data of three patients, other than the ones involved in the parameter learning. The extrapolation performance of the general model was improved, while comparable prediction errors were found for the group-specific approach. This suggests that an online parameter tuning can overcome the limitations of a suboptimal patient stratification, which appeared otherwise a critical issue.
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16
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Abstract
OBJECTIVES This paper aims to present an overview of the medical informatics landscape in Greece, to describe the Greek ehealth background and to highlight the main education and research axes in medical informatics, along with activities, achievements and pitfalls. METHODS With respect to research and education, formal and informal sources were investigated and information was collected and presented in a qualitative manner, including also quantitative indicators when possible. RESULTS Greece has adopted and applied medical informatics education in various ways, including undergraduate courses in health sciences schools as well as multidisciplinary postgraduate courses. There is a continuous research effort, and large participation in EU-wide initiatives, in all the spectrum of medical informatics research, with notable scientific contributions, although technology maturation is not without barriers. Wide-scale deployment of eHealth is anticipated in the healthcare system in the near future. While ePrescription deployment has been an important step, ICT for integrated care and telehealth have a lot of room for further deployment. CONCLUSIONS Greece is a valuable contributor in the European medical informatics arena, and has the potential to offer more as long as the barriers of research and innovation fragmentation are addressed and alleviated.
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Affiliation(s)
| | - N Maglaveras
- Prof. Nicos Maglaveras, Lab of Medical Informatics, P.O. Box 323, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece, E-mail:
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17
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Wang Y, Xue H, Liu S. Applications of systems science in biomedical research regarding obesity and noncommunicable chronic diseases: opportunities, promise, and challenges. Adv Nutr 2015; 6:88-95. [PMID: 25593147 PMCID: PMC4288284 DOI: 10.3945/an.114.007203] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Interest in the application of systems science (SS) in biomedical research, particularly regarding obesity and noncommunicable chronic disease (NCD) research, has been growing rapidly over the past decade. SS is a broad term referring to a family of research approaches that include modeling. As an emerging approach being adopted in public health, SS focuses on the complex dynamic interaction between agents (e.g., people) and subsystems defined at different levels. SS provides a conceptual framework for interdisciplinary and transdisciplinary approaches that address complex problems. SS has unique advantages for studying obesity and NCD problems in comparison to the traditional analytic approaches. The application of SS in biomedical research dates back to the 1960s with the development of computing capacity and simulation software. In recent decades, SS has been applied to addressing the growing global obesity epidemic. There is growing appreciation and support for using SS in the public health field, with many promising opportunities. There are also many challenges and uncertainties, including methodologic, funding, and institutional barriers. Integrated efforts by stakeholders that address these challenges are critical for the successful application of SS in the future.
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Affiliation(s)
- Youfa Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, State University of New York; Buffalo, NY; and
| | - Hong Xue
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, State University of New York; Buffalo, NY; and
| | - Shiyong Liu
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, State University of New York; Buffalo, NY; and,Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu, China
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18
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Stamatakos G, Dionysiou D, Misichroni F, Graf N, van Gool S, Bohle R, Dong F, Viceconti M, Marias K, Sakkalis V, Forgo N, Radhakrishnan R, Byrne H, Guiot C, Buechler P, Neri E, Bucur A, de Bono B, Testi D, Tsiknakis M. Computational Horizons In Cancer (CHIC): Developing Meta- and Hyper-Multiscale Models and Repositories for In Silico Oncology - a Brief Technical Outline of the Project. PROCEEDINGS OF THE 2014 6TH INTERNATIONAL ADVANCED RESEARCH WORKSHOP ON IN SILICO ONCOLOGY AND CANCER INVESTIGATION : THE CHIC PROJECT WORKSHOP (IARWISOCI) : ATHENS, GREECE, 3-4 NOVEMBER 2014. INTERNATIONAL ADVANCED RESEARCH WORKSHOP ON... 2014; 2014. [PMID: 34541587 DOI: 10.1109/iarwisoci.2014.7034630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper briefly outlines the aim, the objectives, the architecture and the main building blocks of the ongoing large scale integrating transatlantic research project CHIC (http://chic-vph.eu/).
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Affiliation(s)
- G Stamatakos
- In Silico Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, Greece
| | - Dimitra Dionysiou
- In Silico Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, Greece
| | - Fay Misichroni
- In Silico Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, Greece
| | - Norbert Graf
- University of Saarland, Pediatric Oncology and Hematology Clinic, Germany
| | - Stefaan van Gool
- Catholic University of Leuven, Pediatric Oncology Clinic, Belgium
| | - Rainar Bohle
- University of Saarland, Dept. of Pathology, Germany
| | | | | | - Kostas Marias
- Foundation for Research and Technology, Hellas, Greece
| | | | | | | | | | | | | | | | - Anca Bucur
- Philips Electronics Nederland B.V., The Netherlands
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