Biomimetic Responsive Nanoconverters with Immune Checkpoint Blockade Plus Antiangiogenesis for Advanced Hepatocellular Carcinoma Treatment.

The first-line treatment for advanced hepatocellular carcinoma (HCC) combines immune checkpoint inhibitors and antiangiogenesis agents to prolong patient survival. Nonetheless, this approach has several limitations, including stringent inclusion criteria and suboptimal response rates that stem from the severe off-tumor side effects and the unfavorable pharmacodynamics and pharmacokinetics of different drugs delivered systemically. Herein, we propose a single-agent smart nanomedicine-based approach that mimics the therapeutic schedule in a targeted and biocompatible manner to elicit robust antitumor immunity in advanced HCC. Our strategy employed pH-responsive carriers, poly(ethylene glycol)-poly(ß-amino esters) amphiphilic block copolymer (PEG-PAEs), for delivering apatinib (an angiogenesis inhibitor), that were surface-coated with plasma membrane derived from engineered cells overexpressing PD-1 proteins (an immune checkpoint inhibitor to block PD-L1). In an advanced HCC mouse model with metastasis, these biomimetic responsive nanoconverters induced significant tumor regression (5/9), liver function recovery, and complete suppression of lung metastasis. Examination of the tumor microenvironment revealed an increased infiltration of immune effector cells (CD8+ and CD4+ T cells) and reduced immunosuppressive cells (myeloid-derived suppressor cells and T regulatory cells) in treated tumors. Importantly, our nanomedicine selectively accumulated in both small and large HCC occupying >50% of the liver volume to exert therapeutic effects with minimal systemic side effects. Overall, these findings highlight the potential of such multifunctional nanoconverters to effectively reshape the tumor microenvironment for advanced HCC treatment.

Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction.

This study aimed to develop a noninvasive predictive model for identifying early postoperative recurrence of hepatocellular carcinoma (within 2 years after surgery) based on contrast-enhanced ultrasound and serum biomarkers. Additionally, the model's validity was assessedthrough internal and external validation. Clinical data were collected from patients who underwent liver resection at the First Hospital of Quanzhou and Mengchao Hepatobiliary Hospital. The data included general information, contrast-enhanced ultrasound parameters, Liver Imaging Reporting and Data System (LI-RADS) classification, and serum biomarkers. The data from Mengchao Hospital were divided into 2 groups, with a ratio of 6:4, to form the modeling and internal validation sets, respectively. On the other hand, the data from the First Hospital of Quanzhou served as the external validation group. The developed model was named the Hepatocellular Carcinoma Early Recurrence (HCC-ER) prediction model. The predictive efficiency of the HCC-ER model was compared with other established models. The baseline characteristics were found to be well-balanced across the modeling, internal validation, and external validation groups. Among the independent risk factors identified for early recurrence, LI-RADS classification, alpha-fetoprotein, and tumor maximum diameter exhibited hazard ratios of 1.352, 1.337, and 1.135 respectively. Regarding predictive accuracy, the HCC-ER, Tumour-Node-Metastasis, Barcelona Clinic Liver Cancer, and China Liver Cancer models demonstrated prediction errors of 0.196, 0.204, 0.201, and 0.200 in the modeling group; 0.215, 0.215, 0.218, and 0.212 in the internal validation group; 0.210, 0.215, 0.216, and 0.221 in the external validation group. Using the HCC-ER model, risk scores were calculated for all patients, and a cutoff value of 50 was selected. This cutoff effectively distinguished the high-risk recurrence group from the low-risk recurrence group in the modeling, internal validation, and external validation groups. However, the calibration curve of the predictive model slightly overestimated the risk of recurrence. The HCC-ER model developed in this study demonstrated high accuracy in predicting early recurrence within 2 years after hepatectomy. It provides valuable information for developing precise treatment strategies in clinical practice and holds considerable promise for further clinical implementation.

A novel model predicted liver cirrhosis constructed by ultrasound and serological in autoimmune liver hepatitis.

To establish a noninvasive model based on two-dimensional shear wave elasticity (2D-SWE) technology, ultrasound feature and serological indicators to predict cirrhosis in autoimmune hepatitis (AIH) and verified. Patients with AIH confirmed by liver biopsy with liver ultrasound and serological examination were collected from January 2019 to May 2022. Patients were divided into cirrhosis and non-cirrhosis groups. Basic indexes, ultrasound indexes and serological indexes were collected. Multivariable logistic regression used for screening independent risk factors predicting cirrhosis, construct the AIH cirrhosis prediction model, named autoimmune hepatitis cirrhosis (AIHC). Determine best cutoff score according to the Youden index, verified the model's predictive efficacy. One hundred forty-six patients were collected. The following indicators were independent risk factors for predicting cirrhosis: LS (OR: 1.416, P = .015), splenomegaly (OR: 10.446, P = .006), complement C4 (OR: 0.020, P = .009). The best cutoff score was 65, with a sensitivity 88.9% and specificity 75.6%; the area under curve was 0.901, AIHC possessed a higher net reclassification index (NRI) and integrated discrimination improvement compared with other indexes, and AIHC had the best clinical decision curve. The AIHC constructed in this study has better predictive efficacy than other noninvasive indexes, and we visualized the model for easy application, which was worth further promotion in clinical practice.

Contrast enhanced ultrasound combined with serology predicts hepatocellular carcinoma recurrence: a retrospective observation cohort study.

Objectives: To construct a novel model based on contrast-enhanced ultrasound (CEUS) and serological biomarkers to predict the early recurrence (ER) of primary hepatocellular carcinoma within 2 years after hepatectomy. Methods: A total of 466 patients who underwent CEUS and curative resection between 2016.1.1 and 2019.1.1 were retrospectively recruited from one institution. The training and testing cohorts comprised 326 and 140 patients, respectively. Data on general characteristics, CEUS Liver Imaging Reporting and Data System (LI-RADS) parameters, and serological were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence, and the Contrast-enhanced Ultrasound Serological (CEUSS) model was constructed. Different models were compared using prediction error and time-dependent area under the receiver operating characteristic curve (AUC). The CEUSS model's performances in ER prediction were assessed. Results: The baseline data of the training and testing cohorts were equal. LI-RADS category, α-fetoprotein level, tumor maximum diameter, total bilirubin level, starting time, iso-time, and enhancement pattern were independent hazards, and their hazards ratios were 1.417, 1.309, 1.133, 1.036, 0.883, 0.985, and 0.70, respectively. The AUCs of CEUSS, BCLC,TNM, and CNLC were 0.706, 0.641, 0.647, and 0.636, respectively, in the training cohort and 0.680, 0.583, 0.607, and 0.597, respectively, in the testing cohort. The prediction errors of CEUSS, BCLC, TNM, and CNLC were 0.202, 0.205, 0.205, and 0.200, respectively, in the training cohort and 0.204, 0.221, 0.219, and 0.211, respectively, in the testing cohort. Conclusions: The CEUSS model can accurately and individually predict ER before surgery and may represent a new tool for individualized treatment.

Navoximod modulates local HSV-1 replication to reshape tumor immune microenvironment for enhanced immunotherapy via an injectable hydrogel.

Oncolytic virotherapy can lead to tumor lysis and systemic anti-tumor immunity, but the therapeutic potential in humans is limited due to the impaired virus replication and the insufficient ability to overcome the immunosuppressive tumor microenvironment (TME). To solve the above problems, we identified that Indoleamine 2, 3-dioxygenase 1 (IDO1) inhibitor Navoximod promoted herpes simplex virus type 1 (HSV-1) replication and HSV-1-mediated oncolysis in tumor cells, making it a promising combination modality with HSV-1-based virotherapy. Thus, we loaded HSV-1 and Navoximod together in an injectable and biocompatible hydrogel (V-Navo@gel) for hepatocellular carcinoma (HCC) virotherapy. The hydrogel formed a local delivery reservoir to maximize the viral replication and distribution at the tumor site with a single-dose injection. Notably, V-Navo@gel improved the disease-free survival time of HCC- bearing mice and protects the mice against tumor recurrence. What's more, V-Navo@gel also showed an effective therapeutic efficacy in the rabbit orthotopic liver cancer model. Mechanistically, we further discovered that our combination strategy entirely reprogramed the TME through single-cell RNA sequencing. All these results collectively indicated that the combination of Navoximod with HSV-1 could boost the viral replication and reshape TME for tumor eradication through the hydrogel reservoir.

Novel model combining contrast-enhanced ultrasound with serology predicts hepatocellular carcinoma recurrence after hepatectomy.

BACKGROUND: Surgery is the primary curative option in patients with hepatocellular carcinoma (HCC). However, recurrence within 2 years is observed in 30%-50% of patients, being a major cause of mortality. AIM: To construct and verify a non-invasive prediction model combining contrast-enhanced ultrasound (CEUS) with serology biomarkers to predict the early recurrence of HCC. METHODS: Records of 744 consecutive patients undergoing first-line curative surgery for HCC in one institution from 2016-2018 were reviewed, and 292 local patients were selected for analysis. General characteristics including gender and age, CEUS liver imaging reporting and data system (LIRADS) parameters including wash-in time, wash-in type, wash-out time, and wash-out type, and serology biomarkers including alanine aminotransferase, aspartate aminotransferase, platelets, and alpha-fetoprotein (AFP) were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence. Then a nomogram called CEUS model was constructed. The CEUS model was then used to predict recurrence at 6 mo, 12 mo, and 24 mo, the cut-off value was calculate by X-tile, and each C-index was calculated. Then Kaplan-Meier curve was compared by log-rank test. The calibration curves of each time were depicted. RESULTS: A nomogram predicting early recurrence (ER), named CEUS model, was formulated based on the results of the multivariate Cox regression analysis. This nomogram incorporated tumor diameter, preoperative AFP level, and LIRADS, and the hazard ratio was 1.123 (95% confidence interval [CI]: 1.041-1.211), 1.547 (95%CI: 1.245-1.922), and 1.428 (95%CI: 1.059-1.925), respectively. The cut-off value at 6 mo, 12 mo, and 24 mo was 100, 80, and 50, and the C-index was 0.748 (95%CI: 0.683-0.813), 0.762 (95%CI: 0.704-0.820), and 0.762 (95%CI: 0.706-0.819), respectively. The model showed satisfactory results, and the calibration at 6 mo was desirable; however, the calibration at 12 and 24 mo should be improved. CONCLUSION: The CEUS model enables the well-calibrated individualized prediction of ER before surgery and may represent a novel tool for biomarker research and individual counseling.