1
|
Kolokotroni E, Abler D, Ghosh A, Tzamali E, Grogan J, Georgiadi E, Büchler P, Radhakrishnan R, Byrne H, Sakkalis V, Nikiforaki K, Karatzanis I, McFarlane NJB, Kaba D, Dong F, Bohle RM, Meese E, Graf N, Stamatakos G. A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin. J Pers Med 2024; 14:475. [PMID: 38793058 PMCID: PMC11122096 DOI: 10.3390/jpm14050475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.
Collapse
Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
| | - Daniel Abler
- Department of Oncology, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland;
- Department of Oncology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Alokendra Ghosh
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Eleftheria Tzamali
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - James Grogan
- Irish Centre for High End Computing, University of Galway, H91 TK33 Galway, Ireland;
| | - Eleni Georgiadi
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
- Biomedical Engineering Department, University of West Attica, 12243 Egaleo, Greece
| | | | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Helen Byrne
- Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK;
| | - Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Katerina Nikiforaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Ioannis Karatzanis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | | | - Djibril Kaba
- Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;
| | - Feng Dong
- Department of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK;
| | - Rainer M. Bohle
- Department of Pathology, Saarland University, 66421 Homburg, Germany;
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany;
| | - Norbert Graf
- Department of Paediatric Oncology and Haematology, Saarland University, 66421 Homburg, Germany;
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Kyroudis CA, Dionysiou DD, Kolokotroni EA, Stamatakos GS. Studying the regression profiles of cervical tumours during radiotherapy treatment using a patient-specific multiscale model. Sci Rep 2019; 9:1081. [PMID: 30705291 PMCID: PMC6355788 DOI: 10.1038/s41598-018-37155-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 12/03/2018] [Indexed: 12/24/2022] Open
Abstract
Apart from offering insight into the biomechanisms involved in cancer, many recent mathematical modeling efforts aspire to the ultimate goal of clinical translation, wherein models are designed to be used in the future as clinical decision support systems in the patient-individualized context. Most significant challenges are the integration of multiscale biodata and the patient-specific model parameterization. A central aim of this study was the design of a clinically-relevant parameterization methodology for a patient-specific computational model of cervical cancer response to radiotherapy treatment with concomitant cisplatin, built around a tumour features-based search of the parameter space. Additionally, a methodological framework for the predictive use of the model was designed, including a scoring method to quantitatively reflect the similarity and bilateral predictive ability of any two tumours in terms of their regression profile. The methodology was applied to the datasets of eight patients. Tumour scenarios in accordance with the available longitudinal data have been determined. Predictive investigations identified three patient cases, anyone of which can be used to predict the volumetric evolution throughout therapy of the tumours of the other two with very good results. Our observations show that the presented approach is promising in quantifiably differentiating tumours with distinct regression profiles.
Collapse
Affiliation(s)
- Christos A Kyroudis
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Dimitra D Dionysiou
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
| | - Eleni A Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| |
Collapse
|
4
|
Little JP, Pettet GJ, Hutmacher DW, Loessner D. SpheroidSim-Preliminary evaluation of a new computational tool to predict the influence of cell cycle time and phase fraction on spheroid growth. Biotechnol Prog 2018; 34:1335-1343. [PMID: 30009492 DOI: 10.1002/btpr.2692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 04/04/2018] [Accepted: 06/28/2018] [Indexed: 11/11/2022]
Abstract
BACKGROUND There is a relative paucity of research that integrates materials science and bioengineering with computational simulations to decipher the intricate processes promoting cancer progression. Therefore, a first-generation computational model, SpheroidSim, was developed that includes a biological data set derived from a bioengineered spheroid model to obtain a quantitative description of cell kinetics. RESULTS SpheroidSim is a 3D agent-based model simulating the growth of multicellular cancer spheroids. Cell cycle time and phases mathematically motivated the population growth. SpheroidSim simulated the growth dynamics of multiple spheroids by individually defining a collection of specific phenotypic traits and characteristics for each cell. Experimental data derived from a hydrogel-based spheroid model were fit to the predictions providing insight into the influence of cell cycle time (CCT) and cell phase fraction (CPF) on the cell population. A comparison of the number of active cells predicted for each analysis showed that the value and method used to define CCT had a greater effect on the predicted cell population than CPF. The model predictions were similar to the experimental results for the number of cells, with the predicted total number of cells varying by 8% and 12%, respectively, compared to the experimental data. CONCLUSIONS SpheroidSim is a first step in developing a biologically based predictive tool capable of revealing fundamental elements in cancer cell physiology. This computational model may be applied to study the effect of the microenvironment on spheroid growth and other cancer cell types that demonstrate a similar multicellular clustering behavior as the population develops. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1335-1343, 2018.
Collapse
Affiliation(s)
- J P Little
- Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - G J Pettet
- Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - D W Hutmacher
- Queensland University of Technology (QUT), Brisbane, QLD, Australia.,Australian Research Centre for Additive Biomanufacturing, QUT, Brisbane, QLD, Australia.,George W Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.,Institute for Advanced Study, Technical University of Munich, Garching, Germany
| | - D Loessner
- Queensland University of Technology (QUT), Brisbane, QLD, Australia.,Barts Cancer Institute, Queen Mary University of London, London, U.K
| |
Collapse
|
5
|
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).
Collapse
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
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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
| | | |
Collapse
|
8
|
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.
Collapse
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.
| |
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
Stamatakos GS, Kolokotroni E, Dionysiou D, Veith C, Kim YJ, Franz A, Marias K, Sabczynski J, Bohle R, Graf N. In silico oncology: exploiting clinical studies to clinically adapt and validate multiscale oncosimulators. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:5545-9. [PMID: 24110993 DOI: 10.1109/embc.2013.6610806] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper presents a brief outline of the notion and the system of oncosimulator in conjunction with a high level description of the basics of its core multiscale model simulating clinical tumor response to treatment. The exemplary case of lung cancer preoperatively treated with a combination of chemotherapeutic agents is considered. The core oncosimulator model is based on a primarily top-down, discrete entity - discrete event multiscale simulation approach. The critical process of clinical adaptation of the model by exploiting sets of multiscale data originating from clinical studies/trials is also outlined. Concrete clinical adaptation results are presented. The adaptation process also conveys important aspects of the planned clinical validation procedure since the same type of multiscale data - although not the same data itself- is to be used for clinical validation. By having exploited actual clinical data in conjunction with plausible literature-based values of certain model parameters, a realistic tumor dynamics behavior has been demonstrated. The latter supports the potential of the specific oncosimulator to serve as a personalized treatment optimizer following an eventually successful completion of the clinical adaptation and validation process.
Collapse
|
11
|
Kaddi CD, Phan JH, Wang MD. Computational nanomedicine: modeling of nanoparticle-mediated hyperthermal cancer therapy. Nanomedicine (Lond) 2014; 8:1323-33. [PMID: 23914967 DOI: 10.2217/nnm.13.117] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Nanoparticle-mediated hyperthermia for cancer therapy is a growing area of cancer nanomedicine because of the potential for localized and targeted destruction of cancer cells. Localized hyperthermal effects are dependent on many factors, including nanoparticle size and shape, excitation wavelength and power, and tissue properties. Computational modeling is an important tool for investigating and optimizing these parameters. In this review, we focus on computational modeling of magnetic and gold nanoparticle-mediated hyperthermia, followed by a discussion of new opportunities and challenges.
Collapse
Affiliation(s)
- Chanchala D Kaddi
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | | | | |
Collapse
|
12
|
Stamatakos G, Dionysiou D, Lunzer A, Belleman R, Kolokotroni E, Georgiadi E, Erdt M, Pukacki J, Rueping S, Giatili S, d'Onofrio A, Sfakianakis S, Marias K, Desmedt C, Tsiknakis M, Graf N. The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context. IEEE J Biomed Health Inform 2014; 18:840-54. [DOI: 10.1109/jbhi.2013.2284276] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, In Silico Oncology Group, 9 Iroon Polytechniou, Zografos, Greece
| | - Dimitra Dionysiou
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | | | - Eleni Kolokotroni
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | - Eleni Georgiadi
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | - Juliusz Pukacki
- Poznan Supercomputing and Networking Center (PSNC), Poznan, Poland
| | - Stefan Rueping
- Fraunhofer IAIS, Schloss Birlinghoven, St. Augustin, Germany
| | - Stavroula Giatili
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | | | - Kostas Marias
- Foundation for Research and Technology Hellas, Heraklion, Greece
| | | | - Manolis Tsiknakis
- Department of Informatics Engineering, TEI Crete and the Computational Medicine Laboratory, Institute of Computer Science, FORTH , Heraklion, Greece
| | - Norbert Graf
- University Hospital of the Saarland, Pediatric Haematology and Oncology, Homburg, Germany
| |
Collapse
|
13
|
Stamatakos GS, Graf N, Radhakrishnan R. Multiscale Cancer Modeling and In Silico Oncology: Emerging Computational Frontiers in Basic and Translational Cancer Research. JOURNAL OF BIOENGINEERING & BIOMEDICAL SCIENCE 2013; 3:e114. [PMID: 30740263 PMCID: PMC6368085 DOI: 10.4172/2155-9538.1000e114] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Georgios S Stamatakos
- In Silico Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografos, Greece
| | - Norbert Graf
- Department of Pediatric Oncology and Hematology, University of Saarland, Homburg, Germany
| | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
14
|
Georgiadi EC, Dionysiou DD, Graf N, Stamatakos GS. Towards in silico oncology: adapting a four dimensional nephroblastoma treatment model to a clinical trial case based on multi-method sensitivity analysis. Comput Biol Med 2012; 42:1064-78. [PMID: 23063290 DOI: 10.1016/j.compbiomed.2012.08.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2012] [Revised: 07/31/2012] [Accepted: 08/24/2012] [Indexed: 11/28/2022]
Abstract
In the past decades a great progress in cancer research has been made although medical treatment is still widely based on empirically established protocols which have many limitations. Computational models address such limitations by providing insight into the complex biological mechanisms of tumor progression. A set of clinically-oriented, multiscale models of solid tumor dynamics has been developed by the In Silico Oncology Group (ISOG), Institute of Communication and Computer Systems (ICCS)-National Technical University of Athens (NTUA) to study cancer growth and response to treatment. Within this context using certain representative parameter values, tumor growth and response have been modeled under a cancer preoperative chemotherapy protocol in the framework of the SIOP 2001/GPOH clinical trial. A thorough cross-method sensitivity analysis of the model has been performed. Based on the sensitivity analysis results, a reasonable adaptation of the values of the model parameters to a real clinical case of bilateral nephroblastomatosis has been achieved. The analysis presented supports the potential of the model for the study and eventually the future design of personalized treatment schemes and/or schedules using the data obtained from in vitro experiments and clinical studies.
Collapse
Affiliation(s)
- Eleni Ch Georgiadi
- In Silico Oncology Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | | | | | | |
Collapse
|
15
|
In silico modelling of treatment-induced tumour cell kill: developments and advances. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2012; 2012:960256. [PMID: 22852024 PMCID: PMC3407630 DOI: 10.1155/2012/960256] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2012] [Revised: 05/10/2012] [Accepted: 05/14/2012] [Indexed: 12/04/2022]
Abstract
Mathematical and stochastic computer (in silico) models of tumour growth and treatment response of the past and current eras are presented, outlining the aims of the models, model methodology, the key parameters used to describe the tumour system, and treatment modality applied, as well as reported outcomes from simulations. Fractionated radiotherapy, chemotherapy, and combined therapies are reviewed, providing a comprehensive overview of the modelling literature for current modellers and radiobiologists to ignite the interest of other computational scientists and health professionals of the ever evolving and clinically relevant field of tumour modelling.
Collapse
|
16
|
Johnson D, Cooper J, McKeever S. TumorML: Concept and requirements of an in silico cancer modelling markup language. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:441-4. [PMID: 22254343 DOI: 10.1109/iembs.2011.6090060] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper describes the initial groundwork carried out as part of the European Commission funded Transatlantic Tumor Model Repositories project, to develop a new markup language for computational cancer modelling, TumorML. In this paper we describe the motivations for such a language, arguing that current state-of-the-art biomodelling languages are not suited to the cancer modelling domain. We go on to describe the work that needs to be done to develop TumorML, the conceptual design, and a description of what existing markup languages will be used to compose the language specification.
Collapse
Affiliation(s)
- David Johnson
- Department of Computer Science, University of Oxford, Wolfson Building, Oxford OX1 3QD, United Kingdom.
| | | | | |
Collapse
|
17
|
Marias K, Dionysiou D, Sakkalis V, Graf N, Bohle RM, Coveney PV, Wan S, Folarin A, Büchler P, Reyes M, Clapworthy G, Liu E, Sabczynski J, Bily T, Roniotis A, Tsiknakis M, Kolokotroni E, Giatili S, Veith C, Messe E, Stenzhorn H, Kim YJ, Zasada S, Haidar AN, May C, Bauer S, Wang T, Zhao Y, Karasek M, Grewer R, Franz A, Stamatakos G. Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm. Interface Focus 2011; 1:450-61. [PMID: 22670213 PMCID: PMC3262443 DOI: 10.1098/rsfs.2010.0037] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2010] [Accepted: 03/07/2011] [Indexed: 12/13/2022] Open
Abstract
The challenge of modelling cancer presents a major opportunity to improve our ability to reduce mortality from malignant neoplasms, improve treatments and meet the demands associated with the individualization of care needs. This is the central motivation behind the ContraCancrum project. By developing integrated multi-scale cancer models, ContraCancrum is expected to contribute to the advancement of in silico oncology through the optimization of cancer treatment in the patient-individualized context by simulating the response to various therapeutic regimens. The aim of the present paper is to describe a novel paradigm for designing clinically driven multi-scale cancer modelling by bringing together basic science and information technology modules. In addition, the integration of the multi-scale tumour modelling components has led to novel concepts of personalized clinical decision support in the context of predictive oncology, as is also discussed in the paper. Since clinical adaptation is an inelastic prerequisite, a long-term clinical adaptation procedure of the models has been initiated for two tumour types, namely non-small cell lung cancer and glioblastoma multiforme; its current status is briefly summarized.
Collapse
Affiliation(s)
- K. Marias
- Institute of Computer Science at FORTH, Heraklion, Greece
| | - D. Dionysiou
- In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
| | - V. Sakkalis
- Institute of Computer Science at FORTH, Heraklion, Greece
| | - N. Graf
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - R. M. Bohle
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - P. V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - S. Wan
- Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - A. Folarin
- Cancer Research Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - P. Büchler
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - M. Reyes
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - G. Clapworthy
- Department of Computer Science & Technology, University of Bedfordshire, Luton, UK
| | - E. Liu
- Department of Computer Science & Technology, University of Bedfordshire, Luton, UK
| | - J. Sabczynski
- Philips Technologie GmbH, Innovative Technologies, Hamburg, Germany
| | - T. Bily
- Faculty of Mathematics and Physics, Department of Applied Mathematics, Charles University in Prague, Prague, Czech Republic
| | - A. Roniotis
- Institute of Computer Science at FORTH, Heraklion, Greece
| | - M. Tsiknakis
- Institute of Computer Science at FORTH, Heraklion, Greece
| | - E. Kolokotroni
- In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
| | - S. Giatili
- In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
| | - C. Veith
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - E. Messe
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - H. Stenzhorn
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - Yoo-Jin Kim
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - S. Zasada
- Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - A. N. Haidar
- Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - C. May
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - S. Bauer
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - T. Wang
- Department of Computer Science & Technology, University of Bedfordshire, Luton, UK
| | - Y. Zhao
- Department of Computer Science & Technology, University of Bedfordshire, Luton, UK
| | - M. Karasek
- Faculty of Mathematics and Physics, Department of Applied Mathematics, Charles University in Prague, Prague, Czech Republic
| | - R. Grewer
- Philips Technologie GmbH, Innovative Technologies, Hamburg, Germany
| | - A. Franz
- Philips Technologie GmbH, Innovative Technologies, Hamburg, Germany
| | - G. Stamatakos
- In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
| |
Collapse
|