首页    期刊浏览 2024年09月21日 星期六
登录注册

文章基本信息

  • 标题:Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy
  • 本地全文:下载
  • 作者:Adrianne L. Jenner ; Munisha Smalley ; David Goldman
  • 期刊名称:iScience
  • 印刷版ISSN:2589-0042
  • 出版年度:2022
  • 卷号:25
  • 期号:6
  • 页码:1-40
  • DOI:10.1016/j.isci.2022.104395
  • 语种:English
  • 出版社:Elsevier
  • 摘要:SummaryOncolytic 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 anex vivotumor 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.Graphical abstractDisplay OmittedHighlights•Ex vivohuman 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 samplesImmunology; Computational bioinformatics; Cancer
国家哲学社会科学文献中心版权所有