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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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2
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Paul D, Komarova NL. Multi-scale network targeting: A holistic systems-biology approach to cancer treatment. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 165:72-79. [PMID: 34428429 DOI: 10.1016/j.pbiomolbio.2021.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 08/05/2021] [Accepted: 08/10/2021] [Indexed: 11/15/2022]
Abstract
The vulnerabilities of cancer at the cellular and, recently, with the introduction of immunotherapy, at the tissue level, have been exploited with variable success. Evaluating the cancer system vulnerabilities at the organismic level through analysis of network topology and network dynamics can potentially predict novel anti-cancer drug targets directed at the macroscopic cancer networks. Theoretical work analyzing the properties and the vulnerabilities of the multi-scale network of cancer needs to go hand-in-hand with experimental research that uncovers the biological nature of the relevant networks and reveals new targetable vulnerabilities. It is our hope that attacking cancer on different spatial scales, in a concerted integrated approach, may present opportunities for novel ways to prevent treatment resistance.
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Affiliation(s)
- Doru Paul
- Medical Oncology, Weill Cornell Medicine, 1305 York Avenue 12th Floor, New York, NY, 10021, USA.
| | - Natalia L Komarova
- Department of Mathematics, University of California Irvine, Irvine, CA, 92697, USA.
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3
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Dakka J, Turilli M, Wright DW, Zasada SJ, Balasubramanian V, Wan S, Coveney PV, Jha S. High-throughput binding affinity calculations at extreme scales. BMC Bioinformatics 2018; 19:482. [PMID: 30577753 PMCID: PMC6302294 DOI: 10.1186/s12859-018-2506-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance. Results We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations. Conclusions As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.
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Affiliation(s)
- Jumana Dakka
- Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA
| | - Matteo Turilli
- Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA
| | - David W Wright
- Centre for Computational Sciences, UCL, 20 Gordon Street, London, UK
| | - Stefan J Zasada
- Centre for Computational Sciences, UCL, 20 Gordon Street, London, UK
| | - Vivek Balasubramanian
- Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA
| | - Shunzhou Wan
- Centre for Computational Sciences, UCL, 20 Gordon Street, London, UK
| | - Peter V Coveney
- Centre for Computational Sciences, UCL, 20 Gordon Street, London, UK
| | - Shantenu Jha
- Department Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, USA. .,Institute for Advanced Computational Sciences, Stony Brook University, NY, USA, Lake Dr, Laufer Center, Stony Brook, NY, USA. .,Computational Science Initiative, Brookhaven National Laboratory, 98 Rochester St, Shirley, NY, USA.
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4
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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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5
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Spjuth O, Karlsson A, Clements M, Humphreys K, Ivansson E, Dowling J, Eklund M, Jauhiainen A, Czene K, Grönberg H, Sparén P, Wiklund F, Cheddad A, Pálsdóttir Þ, Rantalainen M, Abrahamsson L, Laure E, Litton JE, Palmgren J. E-Science technologies in a workflow for personalized medicine using cancer screening as a case study. J Am Med Inform Assoc 2018; 24:950-957. [PMID: 28444384 PMCID: PMC7651972 DOI: 10.1093/jamia/ocx038] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 03/17/2017] [Indexed: 12/25/2022] Open
Abstract
Objective We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings. Materials and Methods We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences. Results The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform. Discussion and Conclusion E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.
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Affiliation(s)
- Ola Spjuth
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala Universitet, Uppsala, Sweden
| | - Andreas Karlsson
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Mark Clements
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Emma Ivansson
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Jim Dowling
- School of Information and Communication Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Alexandra Jauhiainen
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Early Clinical Biometrics, AstraZeneca AB R&D, Gothenburg, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Pär Sparén
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Abbas Cheddad
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Department of Computer Science and Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
| | - Þorgerður Pálsdóttir
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Nordic Information for Action e-Science Center, Stockholm, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Linda Abrahamsson
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Erwin Laure
- School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jan-Eric Litton
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium, Graz, Austria
| | - Juni Palmgren
- Department of Medical Epidemiology and Biostatistics and Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden.,Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
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6
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Protopapa M, Zygogianni A, Stamatakos GS, Antypas C, Armpilia C, Uzunoglu NK, Kouloulias V. Clinical implications of in silico mathematical modeling for glioblastoma: a critical review. J Neurooncol 2017; 136:1-11. [PMID: 29081039 DOI: 10.1007/s11060-017-2650-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 10/22/2017] [Indexed: 01/22/2023]
Abstract
Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology. This paper reviews mathematical models on glioblastoma from the clinical doctor's point of view, with focus on 3D modeling approaches of radiation response of in vivo glioblastomas based on contemporary imaging techniques. As these models aim to provide a clinically useful tool in the era of personalized medicine, the integration of the latest advances in molecular and imaging science and in clinical practice by the in silico models is crucial for their clinical relevance. Our aim is to indicate areas of GBM research that have not yet been addressed by in silico models and to point out evidence that has come up from in silico experiments, which may be worth considering in the clinic. This review examines how close these models have come in predicting the outcome of treatment protocols and in shaping the future of radiotherapy treatments.
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Affiliation(s)
- Maria Protopapa
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Anna Zygogianni
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios S Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Christos Antypas
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Christina Armpilia
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos K Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Vassilis Kouloulias
- Radiation Oncology Unit, 2nd Department of Radiology, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece. .,Radiotherapy Unit, 2nd Department of Radiology, ATTIKON University Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece.
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7
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Kolokotroni E, Dionysiou D, Veith C, Kim YJ, Sabczynski J, Franz A, Grgic A, Palm J, Bohle RM, Stamatakos G. In Silico Oncology: Quantification of the In Vivo Antitumor Efficacy of Cisplatin-Based Doublet Therapy in Non-Small Cell Lung Cancer (NSCLC) through a Multiscale Mechanistic Model. PLoS Comput Biol 2016; 12:e1005093. [PMID: 27657742 PMCID: PMC5033576 DOI: 10.1371/journal.pcbi.1005093] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 08/01/2016] [Indexed: 11/30/2022] Open
Abstract
The 5-year survival of non-small cell lung cancer patients can be as low as 1% in advanced stages. For patients with resectable disease, the successful choice of preoperative chemotherapy is critical to eliminate micrometastasis and improve operability. In silico experimentations can suggest the optimal treatment protocol for each patient based on their own multiscale data. A determinant for reliable predictions is the a priori estimation of the drugs’ cytotoxic efficacy on cancer cells for a given treatment. In the present work a mechanistic model of cancer response to treatment is applied for the estimation of a plausible value range of the cell killing efficacy of various cisplatin-based doublet regimens. Among others, the model incorporates the cancer related mechanism of uncontrolled proliferation, population heterogeneity, hypoxia and treatment resistance. The methodology is based on the provision of tumor volumetric data at two time points, before and after or during treatment. It takes into account the effect of tumor microenvironment and cell repopulation on treatment outcome. A thorough sensitivity analysis based on one-factor-at-a-time and latin hypercube sampling/partial rank correlation coefficient approaches has established the volume growth rate and the growth fraction at diagnosis as key features for more accurate estimates. The methodology is applied on the retrospective data of thirteen patients with non-small cell lung cancer who received cisplatin in combination with gemcitabine, vinorelbine or docetaxel in the neoadjuvant context. The selection of model input values has been guided by a comprehensive literature survey on cancer-specific proliferation kinetics. The latin hypercube sampling has been recruited to compensate for patient-specific uncertainties. Concluding, the present work provides a quantitative framework for the estimation of the in-vivo cell-killing ability of various chemotherapies. Correlation studies of such estimates with the molecular profile of patients could serve as a basis for reliable personalized predictions. Less than 14% of medically treated patients with locally advanced and metastatic non-small cell lung cancer are expected to be alive 5 years after diagnosis. Standard therapeutic strategies include the administration of two drugs in combination, aiming at shrinking the tumor before surgery and improving overall survival. Knowing the sensitivity profile of each patient to different treatment strategies at diagnosis may help choose the most appropriate ones. We develop a methodology for the quantitative estimation of the cytotoxic efficacy of cisplatin-based doublets on cancer cells by applying a simulation model of cancer progression and response. The model incorporates the proliferation cycle, quiescence, differentiation and loss of tumor cells. We evaluate the effect of in vivo microenvironment of real tumors, as expressed by measurable tumor proliferation kinetics, such as how fast the tumor grows, the percentage of cells that are actively dividing, the resistance of stem cells, etc. on treatment outcome so as to derive more accurate estimates. A literature survey guides the selection of values. The methodology is applied to a real clinical dataset of patients. Correlation studies between the derived cytotoxicities and the patients’ molecular profile could lead to predictions of treatment response at the time of diagnosis.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Dimitra Dionysiou
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Christian Veith
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | - Yoo-Jin Kim
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | | | | | - Aleksandar Grgic
- Department of Nuclear Medicine, University of Saarland, Homburg (Saar), Germany
| | - Jan Palm
- Department of Radiotherapy and Radiation Oncology, University of Saarland, Homburg (Saar), Germany
| | - Rainer M. Bohle
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
- * E-mail:
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8
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Kondylakis H, Claerhout B, Keyur M, Koumakis L, van Leeuwen J, Marias K, Perez-Rey D, De Schepper K, Tsiknakis M, Bucur A. The INTEGRATE project: Delivering solutions for efficient multi-centric clinical research and trials. J Biomed Inform 2016; 62:32-47. [PMID: 27224847 DOI: 10.1016/j.jbi.2016.05.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 05/05/2016] [Accepted: 05/17/2016] [Indexed: 10/21/2022]
Abstract
The objective of the INTEGRATE project (http://www.fp7-integrate.eu/) that has recently concluded successfully was the development of innovative biomedical applications focused on streamlining the execution of clinical research, on enabling multidisciplinary collaboration, on management and large-scale sharing of multi-level heterogeneous datasets, and on the development of new methodologies and of predictive multi-scale models in cancer. In this paper, we present the way the INTEGRATE consortium has approached important challenges such as the integration of multi-scale biomedical data in the context of post-genomic clinical trials, the development of predictive models and the implementation of tools to facilitate the efficient execution of postgenomic multi-centric clinical trials in breast cancer. Furthermore, we provide a number of key "lessons learned" during the process and give directions for further future research and development.
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Affiliation(s)
- Haridimos Kondylakis
- Computational BioMedicine Laboratory, FORTH-ICS, N. Plastira 100, Heraklion, Greece.
| | - Brecht Claerhout
- Custodix NV, Kortrijksesteenweg 214b3, Sint-Martens-Latem, Belgium
| | - Mehta Keyur
- German Breast Group, GBG Forschungs GmbH, Geschaeftsfuehrer: Prof. Dr. med. Gunter von Minckwitz, Handelsregister: Amtsgericht Offenbach, HRB 40477 Sitz der Gesellschaft ist Neu-Isenburg, Germany
| | - Lefteris Koumakis
- Computational BioMedicine Laboratory, FORTH-ICS, N. Plastira 100, Heraklion, Greece
| | | | - Kostas Marias
- Computational BioMedicine Laboratory, FORTH-ICS, N. Plastira 100, Heraklion, Greece
| | - David Perez-Rey
- Biomedical Informatics Group, DLSIIS & DIA, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain
| | | | - Manolis Tsiknakis
- Computational BioMedicine Laboratory, FORTH-ICS, N. Plastira 100, Heraklion, Greece; Department of Informatics Engineering, Technological Educational Institute of Crete, Estavromenos 71004, Hearklion, Crete, Greece
| | - Anca Bucur
- PHILIPS Research Europe, High Tech Campus 34, Eindhoven, Netherlands
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9
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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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10
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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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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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12
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Müller S, David R, Marias K, Graf N. The standardized histogram shift of t2 magnetic resonance image (MRI) signal intensities of nephroblastoma does not predict histopathological diagnostic information. Cancer Inform 2015; 14:1-5. [PMID: 25983550 PMCID: PMC4429649 DOI: 10.4137/cin.s19340] [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: 01/07/2015] [Revised: 02/19/2015] [Accepted: 02/25/2015] [Indexed: 11/05/2022] Open
Abstract
The objective of this study is to assess standardized histograms of signal intensities of T2-weighted magnetic resonance image (MRI) modality before and after preoperative chemotherapy for nephroblastoma (Wilms' tumor). All analyzed patients are enrolled in the International Society of Paediatric Oncology (SIOP) 2001/GPOH trial.1 The question to be answered is whether the comparison of the histograms can add new knowledge by comparing them with the histology of the tumor after preoperative chemotherapy. Twenty-three unilateral nephroblastoma cases were analyzed. All patients were examined by MRI before and after preoperative chemotherapy treatment. T2 modalities of the MRIs were selected, and histogram changes were compared to histopathological data available after surgery. Of the 23 tumors, 22 decreased in volume following chemotherapy (median -57.99%; range 15.65 to -90.82%). The preliminary results suggest that standardized histograms of signal intensities of T2 MRI in nephroblastoma is not predicting histopathological diagnostic information and has no implications for the clinical assessment for further chemotherapy.
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Affiliation(s)
- Sabine Müller
- Department of Pediatric Oncology and Hematology, Saarland University, Homburg, Germany
| | - Ruslan David
- Department of Pediatric Oncology and Hematology, Saarland University, Homburg, Germany
| | - Kostas Marias
- Institute of Computer Science at FORTH, Vassilika Vouton, Heraklion, Crete, Greece
| | - Norbert Graf
- Department of Pediatric Oncology and Hematology, Saarland University, Homburg, Germany
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Wang Z, Butner JD, Cristini V, Deisboeck TS. Integrated PK-PD and agent-based modeling in oncology. J Pharmacokinet Pharmacodyn 2015; 42:179-89. [PMID: 25588379 DOI: 10.1007/s10928-015-9403-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Accepted: 01/08/2015] [Indexed: 01/11/2023]
Abstract
Mathematical modeling has become a valuable tool that strives to complement conventional biomedical research modalities in order to predict experimental outcome, generate new medical hypotheses, and optimize clinical therapies. Two specific approaches, pharmacokinetic-pharmacodynamic (PK-PD) modeling, and agent-based modeling (ABM), have been widely applied in cancer research. While they have made important contributions on their own (e.g., PK-PD in examining chemotherapy drug efficacy and resistance, and ABM in describing and predicting tumor growth and metastasis), only a few groups have started to combine both approaches together in an effort to gain more insights into the details of drug dynamics and the resulting impact on tumor growth. In this review, we focus our discussion on some of the most recent modeling studies building on a combined PK-PD and ABM approach that have generated experimentally testable hypotheses. Some future directions are also discussed.
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Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, USA
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14
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Bauer S, Wiest R, Nolte LP, Reyes M. A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 2013; 58:R97-129. [PMID: 23743802 DOI: 10.1088/0031-9155/58/13/r97] [Citation(s) in RCA: 301] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.
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Affiliation(s)
- Stefan Bauer
- Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland.
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15
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Shublaq N, Sansom C, Coveney PV. Patient-specific modelling in drug design, development and selection including its role in clinical decision-making. Chem Biol Drug Des 2013; 81:5-12. [PMID: 22765044 DOI: 10.1111/j.1747-0285.2012.01444.x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Genomics has made enormous progress in the twelve years since the publication of the first draft human genome sequence, but it has not yet been translated into the clinic. Despite spiralling development costs, the number of new drug registrations is not increasing. One reason for this lies in the genetic complexity of disease. Most diseases involve dysregulation in pathways that involve many genes, and many (including most cancers) are themselves genetically heterogeneous. Systems biology involves the multi-level simulation of physiology, cell biology and biochemistry using complex computational techniques. We show here using case studies in cancer and HIV how such computational models, and particularly models based on individual patient data, can be used for drug design and development, and in the selection of the appropriate treatment for a given patient in the face of resistance mutations. If these techniques are to be adopted in routine clinical practice, clinicians will need better training in modern approaches to the integrated analysis of large-scale heterogeneous data and multi-scale models, while developers will need to provide much more usable tools. Investment in computational infrastructure is needed so that results can be returned on clinically relevant timescales and data warehouses designed with data protection as well as accessibility in mind.
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Affiliation(s)
- Nour Shublaq
- Centre for Computational Science & Computational Life & Medical Sciences Network, University College London, London, UK
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16
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Simulating radiotherapy effect in high-grade glioma by using diffusive modeling and brain atlases. J Biomed Biotechnol 2012; 2012:715812. [PMID: 23093856 PMCID: PMC3471023 DOI: 10.1155/2012/715812] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2012] [Revised: 05/18/2012] [Accepted: 05/21/2012] [Indexed: 12/25/2022] Open
Abstract
Applying diffusive models for simulating the spatiotemporal change of concentration of tumour cells is a modern application of predictive oncology. Diffusive models are used for modelling glioblastoma, the most aggressive type of glioma. This paper presents the results of applying a linear quadratic model for simulating the effects of radiotherapy on an advanced diffusive glioma model. This diffusive model takes into consideration the heterogeneous velocity of glioma in gray and white matter and the anisotropic migration of tumor cells, which is facilitated along white fibers. This work uses normal brain atlases for extracting the proportions of white and gray matter and the diffusion tensors used for anisotropy. The paper also presents the results of applying this glioma model on real clinical datasets.
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17
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Wright DW, Wan S, Shublaq N, Zasada SJ, Coveney PV. From base pair to bedside: molecular simulation and the translation of genomics to personalized medicine. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2012; 4:585-98. [PMID: 22899636 DOI: 10.1002/wsbm.1186] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Despite the promises made that genomic sequencing would transform therapy by introducing a new era of personalized medicine, relatively few tangible breakthroughs have been made. This has led to the recognition that complex interactions at multiple spatial, temporal, and organizational levels may often combine to produce disease. Understanding this complexity requires that existing and future models are used and interpreted within a framework that incorporates knowledge derived from investigations at multiple levels of biological function. It also requires a computational infrastructure capable of dealing with the vast quantities of data generated by genomic approaches. In this review, we discuss the use of molecular modeling to generate quantitative and qualitative insights at the smallest scales of the systems biology hierarchy, how it can play an important role in the development of a systems understanding of disease and in the application of such knowledge to help discover new therapies and target existing ones on a personal level.
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Affiliation(s)
- David W Wright
- Centre for Computational Science, University College London, London, UK
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18
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Bauer S, May C, Dionysiou D, Stamatakos G, Buchler P, Reyes M. Multiscale Modeling for Image Analysis of Brain Tumor Studies. IEEE Trans Biomed Eng 2012; 59:25-9. [DOI: 10.1109/tbme.2011.2163406] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Studying the growth kinetics of untreated clinical tumors by using an advanced discrete simulation model. ACTA ACUST UNITED AC 2011. [DOI: 10.1016/j.mcm.2011.05.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Roniotis A, Manikis GC, Sakkalis V, Zervakis ME, Karatzanis I, Marias K. High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases. ACTA ACUST UNITED AC 2011; 16:255-63. [PMID: 21990337 DOI: 10.1109/titb.2011.2171190] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data and the results indicate that prognostication rates can be improved.
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Affiliation(s)
- Alexandros Roniotis
- Institute of Computer Science, Foundation for Research and Technology, GR-700 13 Heraklion, Greece.
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21
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Wan S, Coveney PV. Molecular dynamics simulation reveals structural and thermodynamic features of kinase activation by cancer mutations within the epidermal growth factor receptor. J Comput Chem 2011; 32:2843-52. [DOI: 10.1002/jcc.21866] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2011] [Revised: 05/06/2011] [Accepted: 05/16/2011] [Indexed: 11/10/2022]
Affiliation(s)
- Shunzhou Wan
- Department of Chemistry, Centre for Computational Science, University College London, London WC1H 0AJ, United Kingdom
| | - Peter V. Coveney
- Department of Chemistry, Centre for Computational Science, University College London, London WC1H 0AJ, United Kingdom
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Abstract
The Virtual Physiological Human is synonymous with a programme in computational biomedicine that aims to develop a framework of methods and technologies to investigate the human body as a whole. It is predicated on the transformational character of information technology, brought to bear on that most crucial of human concerns, our own health and well-being.
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Affiliation(s)
- Peter V. Coveney
- Centre for Computational Science, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - Vanessa Diaz
- Department of Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Peter Hunter
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1142, New Zealand
| | - Peter Kohl
- Heart Science Centre, Imperial College, Harefield Hospital, Hill End Road, Harefield UB9 6JH, UK
| | - Marco Viceconti
- Medical Technology Laboratory, Instituto Orthopedico Rizzoli, via di Barbiano 1/10, 40136 Bologna, Italy
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23
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Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2011; 14:354-61. [PMID: 22003719 DOI: 10.1007/978-3-642-23626-6_44] [Citation(s) in RCA: 182] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.
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