非常规 T 细胞在泌尿系统肿瘤中：能抓住就抓住
Unconventional T cells in urological cancers: catch them if you can.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
虚拟队列框架及其在膀胱癌过继性细胞治疗中的应用
英文原题：A virtual cohort framework with applications to adoptive cell therapy in bladder cancer.
查看英文原题
A virtual cohort framework with applications to adoptive cell therapy in bladder cancer.
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中文摘要
即使在相同的治疗下，反应也可能各不相同。虚拟队列建立在可用的、通常有限的数据集之上，能够捕捉这些差异，并有助于发现对广泛个体都有效的治疗方案。在本文中，我们通过改进数据处理、确保虚拟队列可用于根据个体数据将其分层到治疗亚组，以及验证虚拟队列与观察到的数据变异性相匹配，来完善当前的虚拟队列流程。为说明这一点，我们将该流程应用于一个接受吉西他滨（Gem）和OT-1细胞免疫治疗的原位膀胱癌小鼠数据集。我们生成了超过10,000只虚拟小鼠，这些虚拟小鼠复现了肿瘤中三个细胞亚群（癌细胞、T细胞和髓源性抑制细胞）的动态变化，以及四个实验队列（对照、Gem、OT-1和Gem+OT-1）的数据。我们还提供了将该流程用于其他治疗的指南。
展开英文摘要原文
Even under the same treatment, responses can vary. Virtual cohorts build on an available, often limited, dataset can capture these differences and enable the discovery of treatment protocols that work well for a wide variety of individuals.
In this paper, we refined current virtual cohort pipelines by improving data handling, ensuring the virtual cohort can be used to stratify individuals into treatment subgroups based on their data, and validating that the virtual cohort matches the observed data variability. To illustrate, we applied this pipeline to a murine data set of orthotopic bladder cancer treated with gemcitabine (Gem) and immunotherapy with OT-1 cells.
We generated over 10,000 virtual mice that replicate the dynamics of three cell subpopulations in the tumor (cancer cells, T cells, and myeloid-derived suppressor cells) and data from four experimental cohorts (control, Gem, OT-1, and Gem+OT-1).
We also provided a guide for using this pipeline for other treatments.
论文信息
- 作者
- Anderson HG、Bazargan S、Nusbaum DJ、Poch MA、Pilon-Thomas S、Rejniak KA
- 单位
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.United States
- 文献类型
- 预印本
- 期刊
- bioRxiv : the preprint server for biology2026 Mar 10
- 原文标识
- PubMed 41959223 · DOI 10.64898/2026.03.06.710135