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      An Integrated Computational/Experimental Model of Lymphoma Growth

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Non-Hodgkin's lymphoma is a disseminated, highly malignant cancer, with resistance to drug treatment based on molecular- and tissue-scale characteristics that are intricately linked. A critical element of molecular resistance has been traced to the loss of functionality in proteins such as the tumor suppressor p53. We investigate the tissue-scale physiologic effects of this loss by integrating in vivo and immunohistological data with computational modeling to study the spatiotemporal physical dynamics of lymphoma growth. We compare between drug-sensitive Eμ-myc Arf-/- and drug-resistant Eμ-myc p53-/- lymphoma cell tumors grown in live mice. Initial values for the model parameters are obtained in part by extracting values from the cellular-scale from whole-tumor histological staining of the tumor-infiltrated inguinal lymph node in vivo. We compare model-predicted tumor growth with that observed from intravital microscopy and macroscopic imaging in vivo, finding that the model is able to accurately predict lymphoma growth. A critical physical mechanism underlying drug-resistant phenotypes may be that the Eμ-myc p53-/- cells seem to pack more closely within the tumor than the Eμ-myc Arf-/- cells, thus possibly exacerbating diffusion gradients of oxygen, leading to cell quiescence and hence resistance to cell-cycle specific drugs. Tighter cell packing could also maintain steeper gradients of drug and lead to insufficient toxicity. The transport phenomena within the lymphoma may thus contribute in nontrivial, complex ways to the difference in drug sensitivity between Eμ-myc Arf-/- and Eμ-myc p53-/- tumors, beyond what might be solely expected from loss of functionality at the molecular scale. We conclude that computational modeling tightly integrated with experimental data gives insight into the dynamics of Non-Hodgkin's lymphoma and provides a platform to generate confirmable predictions of tumor growth.

          Author Summary

          Non-Hodgkin's lymphoma is a cancer that develops from white blood cells called lymphocytes in the immune system, whose role is to fight disease throughout the body. This cancer can spread throughout the whole body and be very lethal – in the US, one third of patients will die from this disease within five years of diagnosis. Chemotherapy is a usual treatment for lymphoma, but the cancer can become highly resistant to it. One reason is that a critical gene called p53 can become mutated and help the cancer to survive. In this work we investigate how cells with this mutation affect the cancer growth by performing experiments in mice and using a computer model. By inputting the model parameters based on data from the experiments, we are able to accurately predict the growth of the tumor as compared to tumor measurements in living mice. We conclude that computational modeling integrated with experimental data gives insight into the dynamics of Non-Hodgkin's lymphoma, and provides a platform to generate confirmable predictions of tumor growth.

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          Most cited references40

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            Cancer research attracts broad resources and scientists from many disciplines, and has produced some impressive advances in the treatment and understanding of this disease. However, a comprehensive mechanistic view of the cancer process remains elusive. To achieve this it seems clear that one must assemble a physically integrated team of interdisciplinary scientists that includes mathematicians, to develop mathematical models of tumorigenesis as a complex process. Examining these models and validating their findings by experimental and clinical observations seems to be one way to reconcile molecular reductionist with quantitative holistic approaches and produce an integrative mathematical oncology view of cancer progression.
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              This Timeline article charts progress in mathematical modelling of cancer over the past 50 years, highlighting the different theoretical approaches that have been used to dissect the disease and the insights that have arisen. Although most of this research was conducted with little involvement from experimentalists or clinicians, there are signs that the tide is turning and that increasing numbers of those involved in cancer research and mathematical modellers are recognizing that by working together they might more rapidly advance our understanding of cancer and improve its treatment.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                March 2013
                March 2013
                28 March 2013
                : 9
                : 3
                : e1003008
                Affiliations
                [1 ]Department of Bioengineering, University of Louisville, Louisville, Kentucky, United States of America
                [2 ]James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, United States of America
                [3 ]Department of Pathology, University of New Mexico, Albuquerque, New Mexico, United States of America
                [4 ]Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, California, United States of America
                [5 ]Bioengineering, Materials Science & Engineering, Bio-X, Stanford University, Stanford, California, United States of America
                [6 ]Department of Chemical Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America
                University of Notre Dame, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: HBF BRS YLC KI SSG VC. Performed the experiments: BRS YLC KI. Analyzed the data: HBF BRS KI AMR. Contributed reagents/materials/analysis tools: SSG VC. Wrote the paper: HBF BRS YLC KI SSG VC.

                Article
                PCOMPBIOL-D-12-01100
                10.1371/journal.pcbi.1003008
                3610621
                23555235
                6ca56778-3af7-465a-ab74-037680e981c5
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 July 2012
                : 13 February 2013
                Page count
                Pages: 13
                Funding
                This work was supported in part by NCI PSOC MC-START U54CA143907 (VC, SSG, HF), NCI ICBP 1U54CA151668 (VC), NCI ICMIC P50CA114747 (SSG), NCI RO1 CA082214 (SSG), NCI CCNE-TR U54 CA119367 (SSG), CCNE-T U54 U54CA151459 (SSG), and Canary Foundation (SSG), and K99 CA160764 (BRS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Computer Science
                Computer Modeling
                Engineering
                Bioengineering
                Biomedical Engineering
                Mathematics
                Applied Mathematics
                Medicine
                Oncology
                Cancers and Neoplasms
                Hematologic Cancers and Related Disorders
                Lymphomas
                Non-Hodgkin lymphoma

                Quantitative & Systems biology
                Quantitative & Systems biology

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