Integration of Clinical Trial Spatial Multi-omics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery

Abstract 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 the 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 (spQSP) 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 to spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. Validation using spatial proteomics data from Imaging Mass Cytometry (IMC) demonstrated that closer proximity between CD8 T cells and macrophages correlated with non-response. We also compared the model output with Visium spatial transcriptomics (ST) profiling of samples from post-treatment tumor resections in the clinical trial and from another independent study of anti-PD1 monotherapy. ST 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 multi-omics data provides a novel approach for patient outcome prediction and biomarker discovery.