Suppr超能文献

整合数字病理学与数学建模以预测癌症免疫治疗中的空间生物标志物动态变化。

Integrating digital pathology and mathematical modelling to predict spatial biomarker dynamics in cancer immunotherapy.

作者信息

Hutchinson L G, Grimm O

机构信息

Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland.

出版信息

NPJ Digit Med. 2022 Jul 12;5(1):92. doi: 10.1038/s41746-022-00636-3.

Abstract

In oncology clinical trials, on-treatment biopsy samples are taken to confirm the mode of action of new molecules, among other reasons. Yet, the time point of sample collection is typically scheduled according to 'Expert Best Guess'. We have developed an approach integrating digital pathology and mathematical modelling to provide clinical teams with quantitative information to support this decision. Using digitised biopsies from an ongoing clinical trial as the input to an agent-based mathematical model, we have quantitatively optimised and validated the model demonstrating that it accurately recapitulates observed biopsy samples. Furthermore, the validated model can be used to predict the dynamics of simulated biopsies, with applications from protocol design for phase 1-2 studies to the conception of combination therapies, to personalised healthcare.

摘要

在肿瘤学临床试验中,采集治疗期间的活检样本有多种原因,其中包括确认新分子的作用模式。然而,样本采集的时间点通常是根据“专家的最佳猜测”来安排的。我们开发了一种将数字病理学和数学建模相结合的方法,为临床团队提供定量信息以支持这一决策。通过将一项正在进行的临床试验中的数字化活检作为基于主体的数学模型的输入,我们对该模型进行了定量优化和验证,证明它能准确再现观察到的活检样本。此外,经过验证的模型可用于预测模拟活检的动态变化,其应用范围从1-2期研究的方案设计到联合疗法构想,再到个性化医疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a75/9276679/608f55a22d9d/41746_2022_636_Fig1_HTML.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验