Abstract 2312: Integrating spatial multi-omics data with spatial quantitative pharmacology (spQSP) model to simulate human neoadjuvant immunotherapy clinical trial of hepatocellular carcinoma
Authors: Shuming Zhang, Atul Deshpande, Babita K. Verma, Hanwen Wang, Haoyang Mi, Long Yuan, Won Jin Ho, Elizabeth M. Jaffee, Qingfeng Zhu, Robert A. Anders, Mark Yarchoan, Luciane T. Kagohara, Elana J. Fertig, Aleksander S. Popel
Journal: Cancer Research (2024)

Abstract Human clinical trials provided tremendous insights to advance novel systemic therapies and to 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 demonstrated that new combination of immunotherapeutic regimens provide augmented 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 therapies entirely in silico. To facilitate designing dosing regimens and selecting potential predictive biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at cellular and molecular 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. The model is first calibrated using clinical outcomes and measured immune cell densities based on recent neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib (NCT03299946). Then, we validate the results from the spQSP model by leveraging real-world spatial multi-omics data from the same clinical trial. 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 Arginase 1 (Arg1) positive macrophages among non-responders while the reverse trend was observed for responders. 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 and cellular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial. Citation Format: Shuming Zhang, Atul Deshpande, Babita K. Verma, Hanwen Wang, Haoyang Mi, Long Yuan, Won Jin Ho, Elizabeth M. Jaffee, Qingfeng Zhu, Robert A. Anders, Mark Yarchoan, Luciane T. Kagohara, Elana J. Fertig, Aleksander S. Popel. Integrating spatial multi-omics data with spatial quantitative pharmacology (spQSP) model to simulate human neoadjuvant immunotherapy clinical trial of hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2312.