Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy
Adrianne L. Jenner,
Munisha Smalley,
David Goldman,
William F. Goins,
Charles S. Cobbs,
Ralph B. Puchalski,
E. Antonio Chiocca,
Sean Lawler,
Paul Macklin,
Aaron Goldman,
Morgan Craig
Affiliations
Adrianne L. Jenner
Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada; Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
Munisha Smalley
Division of Engineering in Medicine, Brigham and Women’s Hospital, Boston, MA, USA
David Goldman
7730E BlackCrest Pl, Tucson, AZ, USA
William F. Goins
Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
Charles S. Cobbs
Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
Ralph B. Puchalski
Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
E. Antonio Chiocca
Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Sean Lawler
Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Paul Macklin
Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
Aaron Goldman
Division of Engineering in Medicine, Brigham and Women’s Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
Morgan Craig
Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada; Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada; Corresponding author
Summary: Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clinic.
