Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy.
Publication Title
iScience
Document Type
Article
Publication Date
6-17-2022
Keywords
Cancer; Computational bioinformatics; Immunology; washington; swedish; swedish neuro
Abstract
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
Area of Special Interest
Neurosciences (Brain & Spine)
Area of Special Interest
Cancer
Specialty/Research Institute
Neurosciences
Specialty/Research Institute
Oncology
