TY - JOUR
T1 - A cellular automaton model of brain tumor treatment and resistance
AU - Schmitz, Jonathan E.
AU - Kansal, Anuraag R.
AU - Torquato, Salvatore
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2002/12
Y1 - 2002/12
N2 - We have extended an automaton model of brain tumor growth to study the effects of treatment. By varying three treatment parameters, we can simulate tumors that display clinically plausible survival times. Much of our work is dedicated to heterogeneous tumors with both treatment-sensitive and treatment-resistant cells. First, we investigate two-strain systems in which resistant cells are initialized within predominantly sensitive tumors. We find that when resistant cells are not confined to a particular location, they compete more effectively with the sensitive population. Moreover, in this case, the fraction of resistant cells within the tumor is a less important indicator of patient prognosis when compared to the case in which the resistant cells are scattered throughout the tumor. In additional simulations, we investigate tumors that are initially monoclonal and treatment-sensitive, but that undergo resistance-mutations in response to treatment. Here, the tumors with both very frequent and very infrequent mutations develop with more spherical geometries. Tumors with intermediate mutational responses exhibit multi-lobed geometries, as mutant strains develop at localized points on the tumors' surfaces.
AB - We have extended an automaton model of brain tumor growth to study the effects of treatment. By varying three treatment parameters, we can simulate tumors that display clinically plausible survival times. Much of our work is dedicated to heterogeneous tumors with both treatment-sensitive and treatment-resistant cells. First, we investigate two-strain systems in which resistant cells are initialized within predominantly sensitive tumors. We find that when resistant cells are not confined to a particular location, they compete more effectively with the sensitive population. Moreover, in this case, the fraction of resistant cells within the tumor is a less important indicator of patient prognosis when compared to the case in which the resistant cells are scattered throughout the tumor. In additional simulations, we investigate tumors that are initially monoclonal and treatment-sensitive, but that undergo resistance-mutations in response to treatment. Here, the tumors with both very frequent and very infrequent mutations develop with more spherical geometries. Tumors with intermediate mutational responses exhibit multi-lobed geometries, as mutant strains develop at localized points on the tumors' surfaces.
KW - Biosystem
KW - Chemotherapy
KW - Computational modeling
KW - Drug resistance
KW - Glioblastoma multiforme
KW - Heterogeneous tumors
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U2 - 10.1080/1027366031000086674
DO - 10.1080/1027366031000086674
M3 - Article
AN - SCOPUS:0041976124
SN - 1748-670X
VL - 4
SP - 223
EP - 239
JO - Computational and Mathematical Methods in Medicine
JF - Computational and Mathematical Methods in Medicine
IS - 4
ER -