An integrated computational/experimental model of lymphoma growth.

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...

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Autores principales:	Hermann B Frieboes, Bryan R Smith, Yao-Li Chuang, Ken Ito, Allison M Roettgers, Sanjiv S Gambhir, Vittorio Cristini
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ed2a890d2776402589672a8070e89bea
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spelling	oai:doaj.org-article:ed2a890d2776402589672a8070e89bea2021-11-18T05:52:16ZAn integrated computational/experimental model of lymphoma growth.1553-734X1553-735810.1371/journal.pcbi.1003008https://doaj.org/article/ed2a890d2776402589672a8070e89bea2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23555235/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Non-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.Hermann B FrieboesBryan R SmithYao-Li ChuangKen ItoAllison M RoettgersSanjiv S GambhirVittorio CristiniPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 3, p e1003008 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Hermann B Frieboes Bryan R Smith Yao-Li Chuang Ken Ito Allison M Roettgers Sanjiv S Gambhir Vittorio Cristini An integrated computational/experimental model of lymphoma growth.
description	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.
format	article
author	Hermann B Frieboes Bryan R Smith Yao-Li Chuang Ken Ito Allison M Roettgers Sanjiv S Gambhir Vittorio Cristini
author_facet	Hermann B Frieboes Bryan R Smith Yao-Li Chuang Ken Ito Allison M Roettgers Sanjiv S Gambhir Vittorio Cristini
author_sort	Hermann B Frieboes
title	An integrated computational/experimental model of lymphoma growth.
title_short	An integrated computational/experimental model of lymphoma growth.
title_full	An integrated computational/experimental model of lymphoma growth.
title_fullStr	An integrated computational/experimental model of lymphoma growth.
title_full_unstemmed	An integrated computational/experimental model of lymphoma growth.
title_sort	integrated computational/experimental model of lymphoma growth.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/ed2a890d2776402589672a8070e89bea
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An integrated computational/experimental model of lymphoma growth.

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