空间计算模型揭示了肿瘤微环境在利用免疫疗法治疗胶质母细胞瘤中的作用。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Blanche Mongeon, Julien Hébert-Doutreloux, Anudeep Surendran, Elham Karimi, Benoit Fiset, Daniela F Quail, Logan A Walsh, Adrianne L Jenner, Morgan Craig
{"title":"空间计算模型揭示了肿瘤微环境在利用免疫疗法治疗胶质母细胞瘤中的作用。","authors":"Blanche Mongeon, Julien Hébert-Doutreloux, Anudeep Surendran, Elham Karimi, Benoit Fiset, Daniela F Quail, Logan A Walsh, Adrianne L Jenner, Morgan Craig","doi":"10.1038/s41540-024-00419-4","DOIUrl":null,"url":null,"abstract":"<p><p>Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under the current standard of care. Immunotherapies like immune checkpoint inhibitors and oncolytic viruses have been extensively studied to improve this endpoint. However, most thus far have failed. To improve the efficacy of immunotherapies to treat glioblastoma, new single-cell imaging modalities like imaging mass cytometry can be leveraged and integrated with computational models. This enables a better understanding of the tumour microenvironment and its role in treatment success or failure in this hard-to-treat tumour. Here, we implemented an agent-based model that allows for spatial predictions of combination chemotherapy, oncolytic virus, and immune checkpoint inhibitors against glioblastoma. We initialised our model with patient imaging mass cytometry data to predict patient-specific responses and found that oncolytic viruses drive combination treatment responses determined by intratumoral cell density. We found that tumours with higher tumour cell density responded better to treatment. When fixing the number of cancer cells, treatment efficacy was shown to be a function of CD4 + T cell and, to a lesser extent, of macrophage counts. Critically, our simulations show that care must be put into the integration of spatial data and agent-based models to effectively capture intratumoral dynamics. Together, this study emphasizes the use of predictive spatial modelling to better understand cancer immunotherapy treatment dynamics, while highlighting key factors to consider during model design and implementation.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330976/pdf/","citationCount":"0","resultStr":"{\"title\":\"Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies.\",\"authors\":\"Blanche Mongeon, Julien Hébert-Doutreloux, Anudeep Surendran, Elham Karimi, Benoit Fiset, Daniela F Quail, Logan A Walsh, Adrianne L Jenner, Morgan Craig\",\"doi\":\"10.1038/s41540-024-00419-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under the current standard of care. Immunotherapies like immune checkpoint inhibitors and oncolytic viruses have been extensively studied to improve this endpoint. However, most thus far have failed. To improve the efficacy of immunotherapies to treat glioblastoma, new single-cell imaging modalities like imaging mass cytometry can be leveraged and integrated with computational models. This enables a better understanding of the tumour microenvironment and its role in treatment success or failure in this hard-to-treat tumour. Here, we implemented an agent-based model that allows for spatial predictions of combination chemotherapy, oncolytic virus, and immune checkpoint inhibitors against glioblastoma. We initialised our model with patient imaging mass cytometry data to predict patient-specific responses and found that oncolytic viruses drive combination treatment responses determined by intratumoral cell density. We found that tumours with higher tumour cell density responded better to treatment. When fixing the number of cancer cells, treatment efficacy was shown to be a function of CD4 + T cell and, to a lesser extent, of macrophage counts. Critically, our simulations show that care must be put into the integration of spatial data and agent-based models to effectively capture intratumoral dynamics. Together, this study emphasizes the use of predictive spatial modelling to better understand cancer immunotherapy treatment dynamics, while highlighting key factors to consider during model design and implementation.</p>\",\"PeriodicalId\":19345,\"journal\":{\"name\":\"NPJ Systems Biology and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330976/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NPJ Systems Biology and Applications\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1038/s41540-024-00419-4\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Systems Biology and Applications","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s41540-024-00419-4","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

胶质母细胞瘤是成人中最常见、最致命的脑肿瘤,在目前的治疗标准下,中位生存期为 15 个月。为了改善这一终点,人们对免疫检查点抑制剂和溶瘤病毒等免疫疗法进行了广泛研究。然而,迄今为止,大多数研究都以失败告终。为了提高免疫疗法治疗胶质母细胞瘤的疗效,可以利用成像质控细胞仪等新型单细胞成像模式,并将其与计算模型相结合。这样就能更好地了解肿瘤微环境及其对这种难以治疗的肿瘤的治疗成败所起的作用。在这里,我们实施了一个基于代理的模型,该模型可对联合化疗、溶瘤病毒和免疫检查点抑制剂治疗胶质母细胞瘤进行空间预测。我们利用患者成像质谱数据对模型进行了初始化,以预测患者的特异性反应,并发现溶瘤病毒会驱动由瘤内细胞密度决定的联合治疗反应。我们发现,肿瘤细胞密度较高的肿瘤对治疗的反应更好。在固定癌细胞数量的情况下,治疗效果与 CD4 + T 细胞有关,其次与巨噬细胞数量有关。重要的是,我们的模拟结果表明,必须小心整合空间数据和基于代理的模型,才能有效捕捉肿瘤内的动态变化。总之,这项研究强调了使用预测性空间建模来更好地理解癌症免疫疗法的治疗动态,同时突出了在模型设计和实施过程中需要考虑的关键因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies.

Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies.

Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under the current standard of care. Immunotherapies like immune checkpoint inhibitors and oncolytic viruses have been extensively studied to improve this endpoint. However, most thus far have failed. To improve the efficacy of immunotherapies to treat glioblastoma, new single-cell imaging modalities like imaging mass cytometry can be leveraged and integrated with computational models. This enables a better understanding of the tumour microenvironment and its role in treatment success or failure in this hard-to-treat tumour. Here, we implemented an agent-based model that allows for spatial predictions of combination chemotherapy, oncolytic virus, and immune checkpoint inhibitors against glioblastoma. We initialised our model with patient imaging mass cytometry data to predict patient-specific responses and found that oncolytic viruses drive combination treatment responses determined by intratumoral cell density. We found that tumours with higher tumour cell density responded better to treatment. When fixing the number of cancer cells, treatment efficacy was shown to be a function of CD4 + T cell and, to a lesser extent, of macrophage counts. Critically, our simulations show that care must be put into the integration of spatial data and agent-based models to effectively capture intratumoral dynamics. Together, this study emphasizes the use of predictive spatial modelling to better understand cancer immunotherapy treatment dynamics, while highlighting key factors to consider during model design and implementation.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信