Single-cell immune signature for detecting early-stage HCC and early assessing anti-PD-1 immunotherapy efficacy

Alterations in serum metabolic profiles of early-stage hepatocellular carcinoma patients after radiofrequency ablation therapy

OBJECTIVE

To investigate the alterations in serum metabolic profiles and early-stage hepatocellular carcinoma (HCC) patient characteristics after radiofrequency ablation (RFA) therapy. This evaluation aimed to assess treatment effectiveness and identify potential novel approaches and targets for HCC treatment and prognosis monitoring.

METHODS

Untargeted metabolomics technology was employed to analyze serum metabolic profiles in healthy volunteer controls (NCs) and early stage HCC patients before and after RFA therapy. Additionally, Human Metabolome Database and Kyoto Encyclopedia of Genes and Genomes database were used to identify the differential metabolites (DMs) and metabolic pathways. Cystoscape was utilized to construct DM gene networks. Amino acid analyses were performed to validate our findings.

RESULTS

We identified 11, 14, and six DMs between the NC and HCC groups, HCC patients before and after RFA therapy, and post-RFA HCC and NC groups, respectively. The expression levels of these DMs, particularly those of amino acids and lipids, significantly changed. Compared with the NC group, higher levels of L-tyrosine, aspartate, and 18-oxo-oleate were observed in HCC patients, which were significantly reduced in patients after RFA therapy. Meanwhile, HCC patients after RFA therapy had increased levels of L-arginine, phosphatidic acid (20:3), and lysophosphatidyl choline (LPC) (20:4) compared to those before therapy, while their levels before therapy were lower than those of NC. Moreover, most metabolites in the post-RFA and NC groups showed no significant changes in expression, except for L-tyrosine and LPC (16:0). These metabolites could potentially serve as characteristic factors of early-stage HCC patients after RFA therapy. Joint pathway analysis revealed striking changes, mainly in phenylalanine, tyrosine, and tryptophan biosynthesis; alanine, aspartate, and glutamate metabolism; and arginine and aminoacyl-tRNA biosynthesis. Bioinformatics analysis of publicly available data preliminarily identified 187 DM-related metabolic enzymes.

CONCLUSION

Our study proposed novel targets for early-stage HCC treatment, laying the groundwork for improving treatment efficacy and prognosis of early-stage HCC patients.

Overexpression of wild-type HRAS drives non-alcoholic steatohepatitis to hepatocellular carcinoma in mice

Hepatocellular carcinoma (HCC), a prevalent solid carcinoma of significant concern, is an aggressive and often fatal disease with increasing global incidence rates and poor therapeutic outcomes. The etiology and pathological progression of non-alcoholic steatohepatitis (NASH)-related HCC is multifactorial and multistage. However, no single animal model can accurately mimic the full NASH-related HCC pathological progression, posing considerable challenges to transition and mechanistic studies. Herein, a novel conditional inducible wild-type human HRAS overexpressed mouse model (HRAS-HCC) was established, demonstrating 100% morbidity and mortality within approximately one month under normal dietary and lifestyle conditions. Advanced symptoms of HCC such as ascites, thrombus, internal hemorrhage, jaundice, and lung metastasis were successfully replicated in mice. In-depth pathological features of NASH- related HCC were demonstrated by pathological staining, biochemical analyses, and typical marker gene detections. Combined murine anti-PD-1 and sorafenib treatment effectively prolonged mouse survival, further confirming the accuracy and reliability of the model. Based on protein-protein interaction (PPI) network and RNA sequencing analyses, we speculated that overexpression of HRAS may initiate the THBS1-COL4A3 axis to induce NASH with severe fibrosis, with subsequent progression to HCC. Collectively, our study successfully duplicated natural sequential progression in a single murine model over a very short period, providing an accurate and reliable preclinical tool for therapeutic evaluations targeting the NASH to HCC continuum.

CXCL9 Overexpression Predicts Better HCC Response to Anti-PD-1 Therapy and Promotes N1 Polarization of Neutrophils

Background

Anti-programmed death-1 (PD1) antibodies have changed the treatment landscape for hepatocellular carcinoma (HCC) and exhibit promising treatment efficacy. However, the majority of HCCs still do not respond to anti-PD-1 therapy.

Methods

We analyzed the expression of CXCL9 in blood samples from patients who received anti-PD-1 therapy and evaluated its correlation with clinicopathological characteristics and treatment outcomes. Based on the results of Cox regression analysis, a nomogram was established for predicting HCC response to anti-PD-1 therapy. qRT‒PCR and multiple immunofluorescence assays were utilized to analyze the proportions of N1-type neutrophils in vitro and in tumor samples, respectively.

Results

The nomogram showed good predictive efficacy in the training and validation cohorts and may be useful for guiding clinical treatment of HCC patients. We also found that HCC cell-derived CXCL9 promoted N1 polarization of neutrophils in vitro and that AMG487, a specific CXCR3 inhibitor, significantly blocked this process. Moreover, multiple immunofluorescence (mIF) showed that patients with higher serum CXCL9 levels had greater infiltration of the N1 phenotype of tumor-associated neutrophils (TANs).

Conclusion

Our study highlights the critical role of CXCL9 as an effective biomarker of immunotherapy efficacy and in promoting the polarization of N1-type neutrophils; thus, targeting the CXCL9-CXCR3 axis could represent a novel pharmaceutical strategy to enhance immunotherapy for HCC.

Application of artificial intelligence in the prediction of immunotherapy efficacy in hepatocellular carcinoma: Current status and prospects

Artificial Intelligence (AI) has increased as a potent tool in medicine, with promising oncology applications. The emergence of immunotherapy has transformed the treatment terrain for hepatocellular carcinoma (HCC), offering new hope to patients with this challenging malignancy. This article examines the role and future of AI in forecasting the effectiveness of immunotherapy in HCC. We highlight the potential of AI to revolutionize the prediction of therapy response, thus improving patient selection and clinical outcomes. The article further outlines the challenges and future research directions in this emerging field.

Three-dimensional morphology scoring of hepatocellular carcinoma stratifies prognosis and immune infiltration

BACKGROUND

The morphological attributes could serve as pivotal indicators precipitating early recurrence and dismal overall survival in hepatocellular carcinoma (HCC), and quantifying morphological features may better stratify the prognosis of HCC.

OBJECTIVE

To develop a radiomics approach based on 3D tumor morphology features for predicting the prognosis of HCC and identifying differentially expressed genes related to morphology to guide HCC treatment.

MATERIALS AND METHODS

Retrospective study of 357 HCC patients. Radiomic features were extracted from MRI tumor regions; 14 morphology-related features predicted early HCC recurrence and patient stratification via LASSO-Cox modeling. Overall survival (OS) and recurrence-free survival (RFS) were analyzed. RNA sequencing from the Cancer Imaging Archive (TCIA) examined drug sensitivity and stratified HCC using morphological immunity genes, validating recurrence and prognosis.

RESULTS

Patients were split into training (n = 225), test (n = 132), and 50 TCIA dataset cohorts. Two features (Maximum2DdiameterColumn, Sphericity) in Cox regression stratified patients into high/low-risk Morphological Radiological Score (Morph-RS) groups. Significant OS and RFS were seen across all sets. Differentially expressed genes focused on T cell receptor signaling; low-risk group had higher T cells (P = 0.039), B cells (P = 0.041), NK cells (P = 0.018). SN-38, GSK2126458 might treat high-risk morphology. Morphology-immune genes stratified HCC, showing significant RFS/OS differences.

CONCLUSION

Tumor Morph-RS effectively stratifies HCC patients' recurrence and prognosis. Limited immune infiltration seen in Morph-RS high-risk groups signifies the potential of employing tumor morphology as a potent visual biomarker for diagnosing and managing HCC.

A novel molecular subtyping based on multi-omics analysis for prognosis predicting in colorectal melanoma: A 16-year prospective multicentric study

Colorectal melanoma (CRM) is a rare malignant tumor with severe complications, and there is currently a lack of systematic research. We conducted a study that combined proteomics and mutation data of CRM from a cohort of three centers over a 16-years period (2005-2021). The patients were divided into a training set consisting of two centers and a testing set comprising the other center. Unsupervised clustering was conducted on the training set to form two molecular subtypes for clinical characterization and functional analysis. The testing set was used to validate the survival differences between the two subtypes. The comprehensive analysis identified two subtypes of CRM: immune exhausted C1 cluster and DNA repair C2 cluster. The former subtype exhibited characteristics of metabolic disturbance, immune suppression, and poor prognosis, along with APC mutations. A machine learning algorithm named Support Vector Machine (SVM) was applied to predict the classification of CRM patients based on protein expression in the external testing cohort. Two subtypes of primary CRM with clinical and proteomic characteristics provides a reference for subsequent diagnosis and treatments.

The immune response of hepatocellular carcinoma after locoregional and systemic therapies: The available combination option for immunotherapy

Hepatocellular carcinoma (HCC) is associated with a highly heterogeneous immune environment that produces an immune response to various locoregional treatments (LRTs), which in turn affects the effectiveness of immunotherapy. Although LRTs still dominate HCC therapies, 50-60% of patients will ultimately be treated with systemic therapies and might receive those treatments for the rest of their life. TACE, SIRT, and thermal ablation can dramatically increase the immunosuppressive state of HCC, a condition that can be addressed by combination with immunotherapy to restore the activity of lymphocytes and the secretion of cellular immune factors. Immune treatment with locoregional and systemic treatments has dramatically changed the management of HCC. In this review, we examine the research on the changes in the immune microenvironment after locoregional or systemic treatment. We also summarize the regulation of various immune cells and immune factors in the tumor microenvironment and discuss the different infiltration degrees of immune cells and factors on the prognosis of HCC to better compare the efficacy between different treatment methods from the perspective of the tumor microenvironment. This information can be used to help develop treatment options for the upcoming new era of HCC treatment in the future.

Circulating blood biomarkers correlated with the prognosis of advanced triple negative breast cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) can improve survivals of metastatic triple negative breast cancer (mTNBC); however, we still seek circulating blood biomarkers to predict the efficacy of ICIs.

MATERIALS AND METHODS

In this study, we analyzed the data of ICIs treated mTNBC collected in Anhui Medical University affiliated hospitals from 2018 to 2023. The counts of lymphocytes, monocytes, platelets, and ratio indexes (NLR, MLR, PLR) in peripheral blood were investigated via the Kaplan-Meier curves and the Cox proportional-hazards model.

RESULTS

The total of 50 mTNBC patients were treated with ICIs. High level of peripheral lymphocytes and low level of NLR and MLR at baseline and post the first cycle of ICIs play the predictable role of immunotherapies. Lymphocytes counts (HR = 0.280; 95% CI: 0.095-0.823; p = 0.021) and NLR (HR = 1.150; 95% CI: 1.052-1.257; p = 0.002) are significantly correlated with overall survival. High NLR also increases the risk of disease progression (HR = 2.189; 95% CI:1.085-4.414; p = 0.029). When NLR at baseline ≥ 2.75, the hazard of death (HR = 2.575; 95% CI:1.217-5.447; p = 0.013) and disease progression (HR = 2.189; 95% CI: 1.085-4.414; p = 0.029) significantly rise. HER-2 expression and anti-tumor therapy lines are statistically correlated with survivals.

CONCLUSIONS

Before the initiation of ICIs, enriched peripheral lymphocytes and poor neutrophils and NLR contribute to the prediction of survivals.

Inflammation-related prognostic markers in resected hepatocellular carcinoma

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

Overexpression of SH2D1A promotes cancer progression and is associated with immune cell infiltration in hepatocellular carcinoma via bioinformatics and in vitro study

BACKGROUND

SH2 domain containing 1A (SH2D1A) expression has been linked to cancer progression. However, the functions of SH2D1A in hepatocellular carcinoma (HCC) have not been reported.

METHODS

The effects of SH2D1A on the proliferation, migration, and invasion of HCC cells and the related pathways were re-explored in cell models with SH2D1A overexpression using the CCK-8, migration and invasion assays and western blotting. The functions and mechanisms of genes co-expressed with SH2D1A were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The relationship between SH2D1A expression and immune microenvironment features in HCC was explored.

RESULTS

Elevated SH2D1A expression promoted cell proliferation, migration, and invasion, which was related to the overexpression of p-Nf-κB and BCL2A1 protein levels in HCC. SH2D1A expression was related to the immune, stromal, and ESTIMATE scores, and the abundance of immune cells, such as B cells, CD8+ T cells, and T cells. SH2D1A expression was significantly related to the expression of immune cell markers, such as PDCD1, CD8A, and CTLA4 in HCC.

CONCLUSION

SH2D1A overexpression was found to promote cell growth and metastasis via the Nf-κB signaling pathway and may be related to the immune microenvironment in HCC. The findings indicate that SH2D1A can function as a biomarker in HCC.

Synergistic effect of OK-432 in combination with an anti-PD-1 antibody for residual tumors after radiofrequency ablation of hepatocellular carcinoma

BACKGROUND AND AIMS

Radiofrequency ablation (RFA) often results in incomplete ablation for medium-to-large and irregular tumors. To solve this clinical problem, we proposed a new treatment strategy of OK-432 in combination with an anti-programmed cell death protein 1 (αPD-1) antibody for residual tumors after incomplete RFA (iRFA) of hepatocellular carcinoma (HCC).

APPROACH AND RESULTS

The effect of OK-432 on immature dendritic cells (iDCs) was evaluated in vitro. A CCK-8 kit and ELISPOT were used to assess the killing effect of OK-432-induced CD8+ T cells in combination with an αPD-1 antibody on Hepa1-6 cells. We found that OK-432 significantly increased the maturation level of DCs, and OK-432-induced CD8+ T cells in combination with αPD-1 antibody significantly enhanced the function of CD8+ T cells. In the in vivo experiment, HCC model mice were treated with (1) pseudo iRFA + phosphate-buffered saline (PBS); (2) iRFA + PBS; (3) iRFA + OK-432; (4) iRFA + αPD-1; or (5) iRFA + OK-432 + αPD-1. We found that the combined therapy of OK-432 with αPD-1 antibody significantly increased the infiltration and function of CD8+ T cells and significantly decreased the number of FoxP3+ regulatory T cells in residual tumors after iRFA of HCC. Moreover, the smallest tumor volumes and the longest survival were observed in the triple combination treatment (iRFA+OK-432 +αPD-1 antibody) group compared with the other four groups.

CONCLUSIONS

The combined therapy of OK-432 with αPD-1 antibody induced a strong antitumor immune response, which significantly inhibited the residual tumors after iRFA of HCC. This concept may provide a new treatment strategy to increase the curative efficacy of RFA for medium-to-large and irregular HCCs.

AVAILABILITY OF DATA AND MATERIAL

The data of this study are available from the corresponding author on reasonable request.

Dysregulated microRNAs as a biomarker for diagnosis and prognosis of hepatitis B virus-associated hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignancy with a high incidence and fatality rate worldwide. Hepatitis B virus (HBV) infection is one of the most important risk factors for its occurrence and development. Early detection of HBV-associated HCC (HBV-HCC) can improve clinical decision-making and patient outcomes. Biomarkers are extremely helpful, not only for early diagnosis, but also for the development of therapeutics. MicroRNAs (miRNAs), a subset of non-coding RNAs approximately 22 nucleotides in length, have increasingly attracted scientists' attention due to their potential utility as biomarkers for cancer detection and therapy. HBV profoundly impacts the expression of miRNAs potentially involved in the development of hepatocarcinogenesis. In this review, we summarize the current progress on the role of miRNAs in the diagnosis and treatment of HBV-HCC. From a molecular standpoint, we discuss the mechanism by which HBV regulates miRNAs and investigate the exact effect of miRNAs on the promotion of HCC. In the near future, miRNA-based diagnostic, prognostic, and therapeutic applications will make their way into the clinical routine.

Navigating the immunometabolic heterogeneity of B cells in murine hepatocellular carcinoma at single cell resolution

Induction of antitumor immunity is critical for the therapeutic efficacy of hepatocellular carcinoma (HCC) immunotherapy. The cellular metabolic state underpins the effector function of immune cells, yet our understanding of the phenotypic and metabolic heterogeneity of B cells within HCC microenvironment is poorly developed. Herein, we investigated the composition, distribution, phenotype, function and metabolic profiles of B-cell subsets in HCC and adjacent liver tissues from an orthotopic HCC mouse model using single-cell RNA sequencing (scRNA-seq). Our results identified six B-cell clusters, which can be classified into plasma cells and activated and exhausted B cells according to marker expression, functional and temporal distribution. Exhausted B cells exhibited low metabolic activities and impaired effector functions. Activated B and plasma cells showed higher metabolic activity than exhausted B cells, but there were clear differences in their metabolic profiles. In addition, we found that the effector function of exhausted B cells was further diminished in HCC tissues compared with adjacent liver tissues, but their metabolic activity was significantly enhanced. Collectively, we comprehensively characterized the metabolic profile and alterations in B-cell subsets in HCC, which contributes to the understanding of B-cell immunology in HCC and lays the foundation for exploring novel targets in HCC immunotherapy.

Establishment and Validation of a Novel Risk Score for Hepatocellular Carcinoma Based on Bile Acid and Bile Salt Metabolism-Related Genes

Liver cancer is a public disease burden with an increasing incidence rate globally. Bile acid and bile salt's metabolic pathways participate in liver tumorigenesis and regulate the tumor microenvironment. However, there still remains a lack of systematic analysis of the genes related to bile acid and bile salt metabolic pathways in hepatocellular carcinoma (HCC). The mRNA expression data and clinical follow-up information of patients with HCC were obtained from public databases, including The Cancer Genome Atlas, Hepatocellular Carcinoma Database, Gene Expression Omnibus, and IMvigor210. The bile acid and bile salt metabolism-related genes were extracted from Molecular Signatures Database. Univariate Cox and logistic least absolute shrinkage and selection operator regression analyses were conducted to establish the risk model. Single sample gene set enrichment analysis, Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data, and Tumor Immune Dysfunction and Exclusion were adopted to analyze immune status. The efficiency of the risk model was tested using a decision tree and a nomogram. We determined two molecular subtypes based on bile acid and bile salt metabolism-related genes, with the prognosis of the S1 subtype being markedly superior to the S2 subtype. Next, we established a risk model based on the differentially expressed genes between the two molecular subtypes. The high-risk and low-risk groups showed significant differences in the biological pathways, immune score, immunotherapy response, and drug susceptibility. Our results demonstrated the good predictive performance of the risk model in immunotherapy datasets and established that it could be an essential factor affecting the prognosis of HCC. In conclusion, we identified two molecular subtypes based on bile acid and bile salt metabolism-related genes. The risk model established in our study could effectively predict the prognosis of patients with HCC and their immunotherapeutic response, which may contribute to targeted immunotherapy in HCC.

Surface-engineered nanoparticles in cancer immune response and immunotherapy: Current status and future prospects

Cancer immunotherapy is a therapeutic strategy to inhibit tumor growth and metastasis by intervening in the immune response process. Strategies applied to cancer immunotherapy mainly include blocking immune checkpoints, adoptive transfer of engineered immune cells, cytokine therapy, cancer vaccines, and oncolytic virus infection. However, many factors, such as off-target side effects, immunosuppressive cell infiltration and/or upregulation of immune checkpoint expression, cancer cell heterogeneity, and lack of antigen presentation, affect the therapeutic effect of immunotherapy on cancer. To improve the efficacy of targeted immunotherapy and reduce off-target effects, over the past two decades, nanoparticle delivery platforms have been increasingly used in tumor immunotherapy. However, nanoparticles are still subject to biological barriers and biodistribution challenges, which limit their overall clinical potential. This has prompted a series of engineered nanoparticles to overcome specific obstacles and transfer the accumulation of payloads to tumor-infiltrating immune cells. In recent years, new techniques and chemical methods have been employed to modify or functionalize the surfaces of nanoparticles. This review discusses the recent progress of surface-engineered nanoparticles in inducing tumor immune responses and immunotherapy, as well as future directions for the development of next-generation nanomedicines.

Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis

Objective

To provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study.

Methods

In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles.

Results

1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer.

Conclusion

AI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.

Imaging strategies for monitoring the immune response

Real-time monitoring of the immune response can be used to evaluate the immune status of the body and to distinguish immune responders and non-responders, so as to better guide immunotherapy. Through direct labelling of immune cells and imaging specific biomarkers of different cells, the activation status of immune cells and immunosuppressive status of tumor cells can be visualized. The immunotherapeutic regimen can then be adjusted accordingly in a timely manner to improve the efficacy of immunotherapy. In this review, various imaging methods, immune-related imaging probes, current challenges and opportunities are summarized and discussed.

Artificial intelligence-based immunoprofiling serves as a potentially predictive biomarker of nivolumab treatment for advanced hepatocellular carcinoma

Immune checkpoint inhibitors (ICI) have been applied in treating advanced hepatocellular carcinoma (aHCC) patients, but few patients exhibit stable and lasting responses. Moreover, identifying aHCC patients suitable for ICI treatment is still challenged. This study aimed to evaluate whether dissecting peripheral immune cell subsets by Mann-Whitney U test and artificial intelligence (AI) algorithms could serve as predictive biomarkers of nivolumab treatment for aHCC. Disease control group carried significantly increased percentages of PD-L1+ monocytes, PD-L1+ CD8 T cells, PD-L1+ CD8 NKT cells, and decreased percentages of PD-L1+ CD8 NKT cells via Mann-Whitney U test. By recursive feature elimination method, five featured subsets (CD4 NKTreg, PD-1+ CD8 T cells, PD-1+ CD8 NKT cells, PD-L1+ CD8 T cells and PD-L1+ monocytes) were selected for AI training. The featured subsets were highly overlapping with ones identified via Mann-Whitney U test. Trained AI algorithms committed valuable AUC from 0.8417 to 0.875 to significantly separate disease control group from disease progression group, and SHAP value ranking also revealed PD-L1+ monocytes and PD-L1+ CD8 T cells exclusively and significantly contributed to this discrimination. In summary, the current study demonstrated that integrally analyzing immune cell profiling with AI algorithms could serve as predictive biomarkers of ICI treatment.

Blood-based biomarkers for immune-based therapy in advanced HCC: Promising but a long way to go

The introduction of immune checkpoint inhibitors (ICIs) represents a key shift in the management strategy for patients with hepatocellular carcinoma (HCC). However, there is a paucity of predictive biomarkers that facilitate the identification of patients that would respond to ICI therapy. Although several researchers have attempted to resolve the issue, the data is insufficient to alter daily clinical practice. The use of minimally invasive procedures to obtain patient-derived specimen, such as using blood-based samples, is increasingly preferred. Circulating tumor DNA (ctDNA) can be isolated from the blood of cancer patients, and liquid biopsies can provide sufficient material to enable ongoing monitoring of HCC. This is particularly significant for patients for whom surgery is not indicated, including those with advanced HCC. In this review, we summarize the current state of understanding of blood-based biomarkers for ICI-based therapy in advanced HCC, which is promising despite there is still a long way to go.

Reclassifying TNM stage I/II colorectal cancer into two subgroups with different overall survival, tumor microenvironment, and response to immune checkpoint blockade treatment

Background: The traditional TNM staging system is often insufficient to differentiate the survival discrepancies of colorectal cancer (CRC) patients at TNM stage I/II. Our study aimed to reclassify stage I/II CRC patients into several subgroups with different prognoses and explore their suitable therapeutic methods.

Methods: Single-cell RNA (scRNA) sequencing data, bulk RNA sequencing data, and clinicopathological information of CRC patients were enrolled from the TCGA and GEO databases. The tumor microenvironment of CRC tissues was accessed by the ESTIMATE algorithm. The prognostic genes were identified by Cox regression analysis. GO and KEGG analyses were conducted in the DAVID database. GSEA analysis was performed for annotation of the correlated gene sets.

Results: We successfully reclassified stage I/II CRC patients into two subgroups and discovered that patients in cluster-2 underwent worse overall survival than those in cluster-1. GSEA analysis showed that immune-associated gene sets were positively enriched in cluster-2. Besides, the differentially expressed genes (DEGs) between cluster-1 and cluster-2 patients also participated in immune-related biological processes and signaling pathways. Moreover, we found that more immune cells infiltrated the microenvironment of cluster-2 patients compared to that of cluster-1 patients, such as Tregs and tumor-associated macrophages. ScRNA sequencing analysis uncovered that most of the enriched immune-associated signaling in cluster-2 patients was mainly attributed to these upregulated immune cells whose infiltration levels were also high in CRC tissues rather than in normal tissues. In addition, we demonstrated that the expression of immune checkpoint genes was significantly higher in cluster-2 patients compared to cluster-1 patients. ScRNA sequencing analysis revealed that the infiltrated CD8+T cells in CRC were naïve T cells and can be activated into effector T cells after immune checkpoint blockade (ICB) treatment.

Conclusion: TNM stage I/II CRC patients can be divided into two subgroups, which have different overall survival rates, tumor microenvironment, and response to ICB therapy.

Cancer Immunotherapy and Delivery System: An Update

With an understanding of immunity in the tumor microenvironment, immunotherapy turns out to be a powerful tool in the clinic to treat many cancers. The strategies applied in cancer immunotherapy mainly include blockade of immune checkpoints, adoptive transfer of engineered cells, such as T cells, natural killer cells, and macrophages, cytokine therapy, cancer vaccines, and oncolytic virotherapy. Many factors, such as product price, off-target side effects, immunosuppressive tumor microenvironment, and cancer cell heterogeneity, affect the treatment efficacy of immunotherapies against cancers. In addition, some treatments, such as chimeric antigen receptor (CAR) T cell therapy, are more effective in treating patients with lymphoma, leukemia, and multiple myeloma rather than solid tumors. To improve the efficacy of targeted immunotherapy and reduce off-target effects, delivery systems for immunotherapies have been developed in past decades using tools such as nanoparticles, hydrogel matrix, and implantable scaffolds. This review first summarizes the currently common immunotherapies and their limitations. It then synopsizes the relative delivery systems that can be applied to improve treatment efficacy and minimize side effects. The challenges, frontiers, and prospects for applying these delivery systems in cancer immunotherapy are also discussed. Finally, the application of these approaches in clinical trials is reviewed.