人体临床试验是推进新型全身疗法、改善癌症患者治疗效果的重要工具。由于耐药治疗选择有限，推进肝细胞癌（HCC）新治疗方法的需求十分迫切。最近的人体临床试验表明，新型联合免疫疗法方案在一部分患者中取得了前所未有的临床反应。可以通过数学方程来模拟肿瘤，并描述细胞与分子相互作用的计算方法，正成为模拟治疗在体内的作用的有希望的工具。为了便于设计给药方案和确定潜在生物标志物，我们开发了一个新的计算模型，用于在HCC中以器官尺度跟踪肿瘤进展，同时反映组织尺度上肿瘤的空间异质性。这个计算模型被称为空间定量系统药理学（spQSP）平台，也被设计用于模拟联合免疫疗法的效果。我们通过利用联合应用PD-1免疫疗法和多靶点酪氨酸激酶抑制剂（TKI）卡博替尼的新辅助HCC临床试验的真实空间多组学数据来验证spQSP系统的结果。模型结果与成像质谱细胞学（IMC）的空间数据进行比较。IMC数据和模拟结果都暗示，非反应者的CD8 T细胞和巨噬细胞之间更近距离接触，而反应者则相反。分析结果还表明，反应者样品中免疫细胞更分散、癌细胞更少散布。我们还将模型输出与原始临床试验中术后肿瘤切除标本的Visium空间转录组学分析结果进行了比较。空间转录组学数据和模拟结果均确认了肿瘤血管结构和TGFβ在肿瘤和免疫细胞相互作用中的作用。据我们所知，这是第一个以人体临床试验的高通量空间多组学数据为基础的用于分子尺度虚拟临床试验的空间肿瘤模型。

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.