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IntroductionThe limited response to immune checkpoint blockades (ICBs) in patients with hepatocellular carcinoma (HCC) highlights the urgent need for broadening the scope of current immunotherapy approaches. Lenvatinib has been shown a potential synergistic effect with ICBs. This study investigated the optimal method for combining these two therapeutic agents and the underlying mechanisms.MethodsThe effect of lenvatinib at three different doses on promoting tissue perfusion and vascular normalization was evaluated in both immunodeficient and immunocompetent mouse models. The underlying mechanisms were investigated by analyzing the vascular morphology of endothelial cells and pericytes. The enhanced immune infiltration of optimal-dose lenvatinib and its synergistic effect of lenvatinib and anti-PD-1 antibody was further evaluated by flow cytometry and immunofluorescence imaging.ResultsThere was an optimal dose that superiorly normalized tumor vasculature and increased immune cell infiltration in both immunodeficient and immunocompetent mouse models. An adequate concentration of lenvatinib strengthened the integrity of human umbilical vein endothelial cells by inducing the formation of the NRP-1-PDGFRβ complex and activating the Crkl-C3G-Rap1 signaling pathway in endothelial cells. Additionally, it promoted the interaction between endothelial cells and pericytes by inducing tyrosine-phosphorylation in pericytes. Furthermore, the combination of an optimal dose of lenvatinib and an anti-PD-1 antibody robustly suppressed tumor growth.ConclusionsOur study proposes a mechanism that explains how the optimal dose of lenvatinib induces vascular normalization and confirms its enhanced synergistic effect with ICBs.

Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the application of artificial intelligence (AI) in the medical field in recent years, an increasing number of studies have indicated that the efficacy of immunotherapy can be better anticipated with the help of AI technology to reach precision medicine. This article focuses on the current prediction models based on information from histopathological slides, imaging-omics, genomics, and proteomics, and reviews their research progress and applications. Furthermore, we also discuss the existing challenges encountered by AI in the field of immunotherapy, as well as the future directions that need to be improved, to provide a point of reference for the early implementation of AI-assisted diagnosis and treatment systems in the future.