An Agent-Based Model of Combination Oncolytic Viral Therapy and Anti-PD-1 Immunotherapy Reveals the Importance of Spatial Location When Treating Glioblastoma

被引:19
作者
Storey, Kathleen M. [1 ]
Jackson, Trachette L. [2 ]
机构
[1] Lafayette Coll, Dept Math, Easton, PA 18042 USA
[2] Univ Michigan, Dept Math, Ann Arbor, MI 48109 USA
关键词
mathematical modeling; agent-based modeling; oncolytic viral therapy; immune checkpoint inhibitor; combination therapy; glioblastoma; MATHEMATICAL-MODEL; CELLULAR-AUTOMATA; TUMOR; VIROTHERAPY; VIRUSES;
D O I
10.3390/cancers13215314
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary</p> A combination of oncolytic viral therapy and immunotherapy provides an alternative option to the standard of care for treating the lethal brain tumor glioblastoma (GBM). Although this combination therapy shows promise, there are many unknown questions regarding how the tumor landscape and spatial dosing strategies impact the effectiveness of the treatment. Our study aims to shed light on these questions using a novel spatially explicit computational model of GBM response to treatment. Our results suggest that oncolytic viral dosing in the location of highest tumor cell density leads to substantial tumor size reduction over viral dosing in the center of the tumor. These results can help to inform future clinical trials and more effective treatment strategies for oncolytic viral therapy in GBM patients.</p> Oncolytic viral therapies and immunotherapies are of growing clinical interest due to their selectivity for tumor cells over healthy cells and their immunostimulatory properties. These treatment modalities provide promising alternatives to the standard of care, particularly for cancers with poor prognoses, such as the lethal brain tumor glioblastoma (GBM). However, uncertainty remains regarding optimal dosing strategies, including how the spatial location of viral doses impacts therapeutic efficacy and tumor landscape characteristics that are most conducive to producing an effective immune response. We develop a three-dimensional agent-based model (ABM) of GBM undergoing treatment with a combination of an oncolytic Herpes Simplex Virus and an anti-PD-1 immunotherapy. We use a mechanistic approach to model the interactions between distinct populations of immune cells, incorporating both innate and adaptive immune responses to oncolytic viral therapy and including a mechanism of adaptive immune suppression via the PD-1/PD-L1 checkpoint pathway. We utilize the spatially explicit nature of the ABM to determine optimal viral dosing in both the temporal and spatial contexts. After proposing an adaptive viral dosing strategy that chooses to dose sites at the location of highest tumor cell density, we find that, in most cases, this adaptive strategy produces a more effective treatment outcome than repeatedly dosing in the center of the tumor.</p>
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页数:19
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共 35 条
  • [1] Combining cellular automata and lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition
    Alemani, Davide
    Pappalardo, Francesco
    Pennisi, Marzio
    Motta, Santo
    Brusic, Vladimir
    [J]. JOURNAL OF IMMUNOLOGICAL METHODS, 2012, 376 (1-2) : 55 - 68
  • [2] Adult Glioblastoma
    Alexander, Brian M.
    Cloughesy, Timothy F.
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2017, 35 (21) : 2402 - +
  • [3] Agent-based models in translational systems biology
    An, Gary
    Mi, Qi
    Dutta-Moscato, Joyeeta
    Vodovotz, Yoram
    [J]. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE, 2009, 1 (02) : 159 - 171
  • [4] Agent-Based Model of Therapeutic Adipose-Derived Stromal Cell Trafficking during Ischemia Predicts Ability To Roll on P-Selectin
    Bailey, Alexander M.
    Lawrence, Michael B.
    Shang, Hulan
    Katz, Adam J.
    Peirce, Shayn M.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2009, 5 (02)
  • [5] A Mathematical Model to Elucidate Brain Tumor Abrogation by Immunotherapy with T11 Target Structure
    Banerjee, Sandip
    Khajanchi, Subhas
    Chaudhuri, Swapna
    [J]. PLOS ONE, 2015, 10 (05):
  • [6] Agent-based modeling: A revolution?
    Bankes, SC
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2002, 99 : 7199 - 7200
  • [7] In vitro and in silico multidimensional modeling of oncolytic tumor virotherapy dynamics
    Berg, David R.
    Offord, Chetan P.
    Kemler, Iris
    Ennis, Matthew K.
    Chang, Lawrence
    Paulik, George
    Bajzer, Zeljko
    Neuhauser, Claudia
    Dingli, David
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2019, 15 (03)
  • [8] Phase I Study of Single-Agent Anti-Programmed Death-1 (MDX-1106) in Refractory Solid Tumors: Safety, Clinical Activity, Pharmacodynamics, and Immunologic Correlates
    Brahmer, Julie R.
    Drake, Charles G.
    Wollner, Ira
    Powderly, John D.
    Picus, Joel
    Sharfman, William H.
    Stankevich, Elizabeth
    Pons, Alice
    Salay, Theresa M.
    McMiller, Tracee L.
    Gilson, Marta M.
    Wang, Changyu
    Selby, Mark
    Taube, Janis M.
    Anders, Robert
    Chen, Lieping
    Korman, Alan J.
    Pardoll, Drew M.
    Lowy, Israel
    Topalian, Suzanne L.
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2010, 28 (19) : 3167 - 3175
  • [9] De Pillis L. G., 2006, COMPUT MATH METHODS, V7, P159, DOI DOI 10.1080/10273660600968978
  • [10] Dutta-Moscato Joyeeta, 2014, Front Bioeng Biotechnol, V2, P18, DOI 10.3389/fbioe.2014.00018