The current treatment strategies in advanced malignancies remain limited. Notably, immunotherapies have raised hope for a successful control of these advanced diseases, but their therapeutic responses are suboptimal and vary considerably among individuals. Tumor-associated macrophages (TAMs) are a major component of the tumor microenvironment (TME) and are often correlated with poor prognosis and therapy resistance, including immunotherapies. Thus, a deeper understanding of the complex roles of TAMs in immunotherapy regulation could provide new insight into the TME. Furthermore, targeting of TAMs is an emerging field of interest due to the hope that these strategies will synergize with current immunotherapies. In this review, we summarize recent studies investigating the involvement of TAMs in immune checkpoint inhibition, tumor vaccines and adoptive cell transfer therapies, and discuss the therapeutic potential of targeting TAMs as an adjuvant therapy in tumor immunotherapies.

Hepatocellular carcinoma (HCC) is one of the most common cancers and a leading cause of death worldwide. Due to latent liver disease, late diagnosis, and nonresponse to systemic treatments, surgical resection and/or biopsy specimens are still generally considered as the gold standard by clinicians for clinical decision-making until now. Since the conventional tissue biopsy is invasive and contains small tissue samples, it is unable to represent tumor heterogeneity or monitor dynamic tumor progression. Therefore, it is imperative to find a new less invasive or noninvasive diagnostic strategy to detect HCC at an early stage and to monitor HCC recurrence. Over the past years, a new diagnostic concept known as “liquid biopsy” has emerged with substantial attention. Liquid biopsy is noninvasive and allows repeated analyses to monitor tumor recurrence, metastasis or treatment responses in real time. With the advanced development of new molecular techniques, HCC circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) detection have achieved interesting and encouraging results. In this review, we focus on the clinical applications of CTCs and ctDNA as key components of liquid biopsy in HCC patients.

Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

Objective We aimed to elucidate the mutual regulation mechanism of ubiquitin-specific protease 22 (USP22) and hypoxia inducible factor-1α (HIF1α), and the mechanism they promote the stemness of hepatocellular carcinoma (HCC) cells under hypoxic conditions. Design Cell counting, migration, self-renewal ability, chemoresistance and expression of stemness genes were established to detect the stemness of HCC cells. Immunoprecipitation, ubiquitination assay and chromatin immunoprecipitation assay were used to elucidate the mutual regulation mechanism of USP22 and HIF1α. HCC patient samples and The Cancer Genome Atlas data were used to demonstrate the clinical significance. In vivo USP22-targeting experiment was performed in mice bearing HCC. Results USP22 promotes hypoxia-induced HCC stemness and glycolysis by deubiquitinating and stabilising HIF1α. As direct target genes of HIF1α, USP22 and TP53 can be transcriptionally upregulated by HIF1α under hypoxic conditions. In TP53 wild-type HCC cells, HIF1α induced TP53-mediated inhibition of HIF1α-induced USP22 upregulation. In TP53-mutant HCC cells, USP22 and HIF1α formed a positive feedback loop and promote the stemness of HCC. HCC patients with a loss-of-function mutation at TP53 and high USP22 and/or HIF1α expression tend to have a worse prognosis. The USP22-targeting lipopolyplexes caused high tumour inhibition and high sorafenib sensitivity in mice bearing HCC. Conclusion USP22 promotes hypoxia-induced HCC stemness by a HIF1α/USP22 positive feedback loop on TP53 inactivation. USP22 is a promising target for the HCC therapy.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and has been acknowledged as a leading cause of death among cirrhosis patients. Difficulties in early diagnosis and heterogeneity are obstacles to effective treatment, especially for advanced HCC. Liver transplantation (LT) is considered the best therapy for HCC. Although many biomarkers are being proposed, alpha-fetoprotein (AFP), which was identified over 60 years ago, remains the most utilized. Recently, much hope has been placed in the immunogenicity of AFP to develop novel therapies, such as AFP vaccines and AFP-specific adoptive T-cell transfer (ACT). This review summarizes the performance of AFP as a biomarker for HCC diagnosis and prognosis, as well as its correlation with molecular classes. In addition, the role of AFP in LT is also described. Finally, we highlight the mechanism and application prospects of two immune therapies (AFP vaccine and ACT) for HCC. In general, our review points out the prevalence of AFP in HCC, accompanied by some controversies and novel directions for future research.

Background: Liver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients’ multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features. Methods: Our study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model. Results: The STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors’ consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors’ consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model’s generalization ability. Conclusions: We incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.