PDF(Pubmed)
Abstract：
Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders\' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.
摘要：
人体临床试验是推进新型全身疗法改善癌症患者治疗结果的重要工具。少数持久的治疗方案导致了在肝细胞癌（HCC）中推进新疗法的迫切需要。最近的人体临床试验表明，新的联合免疫治疗方案在一部分患者中提供了前所未有的临床反应。可以从描述细胞和分子相互作用的数学方程中模拟肿瘤的计算方法正在成为完全在计算机上模拟治疗影响的有前途的工具。为了便于设计给药方案和识别潜在的生物标志物，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，同时反映HCC组织尺度的肿瘤空间异质性。这种计算模型被称为空间定量系统药理学（spQSP）平台，它也被设计为模拟联合免疫疗法的效果。然后，我们通过利用来自新辅助HCC临床试验的现实空间多组学数据，结合抗PD-1免疫疗法和多靶向酪氨酸激酶抑制剂（TKI）卡博替尼，验证了spQSP系统的结果。将模型输出与来自成像质谱细胞计量(IMC)的空间数据进行比较。IMC数据和模拟结果都表明在非应答者中CD8T细胞和巨噬细胞之间更接近,而对于应答者观察到相反的趋势。这些分析还暗示免疫细胞在应答者样本中更广泛的分散和更少的分散的癌细胞。我们还将模型输出与原始临床试验中治疗后肿瘤切除样品的Visium空间转录组学分析进行了比较。空间转录组数据和模拟结果都确定了肿瘤脉管系统和TGFβ的空间模式在肿瘤和免疫细胞相互作用中的作用。据我们所知,这是第一个用于分子尺度虚拟临床试验的空间肿瘤模型,该模型基于来自人体临床试验的高通量空间多组学数据.
