Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

• Published in: iScience 2022-06, Vol.25 (6), p.104395-104395, Article 104395
• Main Authors: Jenner, Adrianne L., Smalley, Munisha, Goldman, David, Goins, William F., Cobbs, Charles S., Puchalski, Ralph B., Chiocca, E. Antonio, Lawler, Sean, Macklin, Paul, Goldman, Aaron, Craig, Morgan
• Format: Article
• Language: English
• Subjects: Cancer; Computational bioinformatics; Immunology
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2022.104395

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. [Display omitted] •Ex vivo human glioblastoma model tracks oncolytic herpes simplex virus-1 efficacy•Computational model predicts treatment efficacy in heterogeneous glioblastoma samples•Simulations show performance of treatment relies on tumor cell density•Findings validated in heterogeneous patient samples Immunology; Computational bioinformatics; Cancer