Due to the lack of treatment options, there remains a need to advance new therapeutics in hepatocellular carcinoma (HCC). The traditional approach moves from initial molecular discovery through animal models to human trials to advance novel systemic therapies that improve treatment outcomes for patients with cancer. Computational methods that simulate tumors mathematically to describe cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico, potentially greatly accelerating delivery of new therapeutics to patients. To facilitate the design of dosing regimens and identification of potential biomarkers for immunotherapy, we developed a new computational model to track tumor progression at the organ scale while capturing the spatial heterogeneity of the tumor in HCC. This computational model of spatial quantitative systems pharmacology was designed to simulate the effects of combination immunotherapy. The model was initiated using literature-derived parameter values and fitted to the specifics of HCC. Model validation was done through comparison with spatial multiomics data from a neoadjuvant HCC clinical trial combining anti-PD1 immunotherapy and a multitargeted tyrosine kinase inhibitor cabozantinib. Validation using spatial proteomics data from imaging mass cytometry demonstrated that closer proximity between CD8 T cells and macrophages correlated with nonresponse. We also compared the model output with Visium spatial transcriptomics profiling of samples from posttreatment tumor resections in the clinical trial and from another independent study of anti-PD1 monotherapy. Spatial transcriptomics data confirmed simulation results, suggesting the importance of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. Our findings demonstrate that incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides a novel approach for patient outcome prediction and biomarker discovery. Significance: Incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides an effective approach for patient outcome prediction and biomarker discovery.

中文翻译：

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临床试验空间多组学分析和虚拟临床试验的集成可实现免疫治疗反应预测和生物标志物发现。


由于缺乏治疗选择，仍然需要推进肝细胞癌（HCC）的新疗法。传统方法从最初的分子发现到动物模型再到人体试验，以推进新的系统疗法，改善癌症患者的治疗结果。以数学方式模拟肿瘤以描述细胞和分子相互作用的计算方法正在成为完全在计算机中模拟治疗影响的有前途的工具，有可能大大加速向患者提供新疗法。为了促进免疫治疗的给药方案设计和潜在生物标志物的识别，我们开发了一种新的计算模型来跟踪器官尺度的肿瘤进展，同时捕获 HCC 肿瘤的空间异质性。这种空间定量系统药理学的计算模型旨在模拟联合免疫疗法的效果。该模型是使用文献得出的参数值启动的，并适合 HCC 的具体情况。通过与结合抗 PD1 免疫疗法和多靶点酪氨酸激酶抑制剂卡博替尼的新辅助 HCC 临床试验的空间多组学数据进行比较来完成模型验证。使用成像质量流式细胞仪的空间蛋白质组学数据进行的验证表明，CD8 T 细胞和巨噬细胞之间的距离越近与无反应相关。我们还将模型输出与 Visium 空间转录组学分析对临床试验中治疗后肿瘤切除样本以及另一项抗 PD1 单一疗法独立研究的样本进行了比较。 空间转录组学数据证实了模拟结果，表明肿瘤血管系统和 TGFβ 的空间模式在肿瘤和免疫细胞相互作用中的重要性。我们的研究结果表明，将数学建模和计算机模拟与高通量空间多组学数据相结合，为患者结果预测和生物标志物发现提供了一种新方法。意义：将数学建模和计算机模拟与高通量空间多组学数据相结合，为患者结果预测和生物标志物发现提供了一种有效的方法。