Background

Heterogeneity of hepatocellular carcinoma (HCC) presents significant challenges for therapeutic strategies and necessitates combinatorial treatment approaches to counteract suppressive behavior of tumor microenvironment and achieve improved outcomes. Here, we employed cytokines to induce memory-like behavior in natural killer (NK) cells, thereby enhancing their cytotoxicity against HCC. Additionally, we evaluated the potential benefits of combining sorafenib with this newly developed memory-like NK cell (pNK) immunochemotherapy in a preclinical model.

Methods

HCC tumors were grown in SD rats using subcapsular implantation. Interleukin 12/18 cytokines were supplemented to NK cells to enhance cytotoxicity through memory activation. Tumors were diagnosed using MRI, and animals were randomly assigned to control, pNK immunotherapy, sorafenib chemotherapy, or combination therapy groups. NK cells were delivered locally via the gastrointestinal tract, while sorafenib was administered systemically. Therapeutic responses were monitored with weekly multi-parametric MRI scans over three weeks. Afterward, tumor tissues were harvested for histopathological analysis. Structural and functional changes in tumors were evaluated by analyzing MRI and histopathology data using ANOVA and pairwise T-test analyses.

Results

The tumors were allowed to grow for six days post-cell implantation before treatment commenced. At baseline, tumor diameter averaged 5.27 mm without significant difference between groups (p = 0.16). Both sorafenib and combination therapy imposed greater burden on tumor dimensions compared to immunotherapy alone in the first week. By the second week of treatment, combination therapy had markedly expanded its therapeutic efficacy, resulting in the most significant tumor regression observed (6.05 ± 1.99 vs. 13.99 ± 8.01 mm). Histological analysis demonstrated significantly improved cell destruction in the tumor microenvironment associated with combination treatment (63.79%). Interestingly, we observed fewer viable tumor regions in the sorafenib group (38.9%) compared to the immunotherapy group (45.6%). Notably, there was a significantly higher presence of NK cells in the tumor microenvironment with combination therapy (34.79%) compared to other groups (ranging from 2.21 to 26.50%). Although the tumor sizes in the monotherapy groups were similar, histological analysis revealed a stronger response in pNK cell immunotherapy group compared to the sorafenib group.

Conclusions

Experimental results indicated that combination therapy significantly enhanced treatment response, resulting in substantial tumor growth reduction in alignment with histological analysis.

Background

Hepatocellular carcinoma (HCC) is a common liver malignancy with limited treatment options. Previous studies expressed the potential synergy of sorafenib and NK cell immunotherapy as a promising approach against HCC. MRI is commonly used to assess response of HCC to therapy. However, traditional MRI-based metrics for treatment efficacy are inadequate for capturing complex changes in the tumor microenvironment, especially with immunotherapy. In this study, we investigated potent MRI radiomics analysis to non-invasively assess early responses to combined sorafenib and NK cell therapy in a HCC rat model, aiming to predict multiple treatment outcomes and optimize HCC treatment evaluations.

Methods

Sprague Dawley (SD) rats underwent tumor implantation with the N1-S1 cell line. Tumor progression and treatment efficacy were assessed using MRI following NK cell immunotherapy and sorafenib administration. Radiomics features were extracted, processed, and selected from both T1w and T2w MRI images. The quantitative models were developed to predict treatment outcomes and their performances were evaluated with area under the receiver operating characteristic (AUROC) curve. Additionally, multivariable linear regression models were constructed to determine the correlation between MRI radiomics and histology, aiming for a noninvasive evaluation of tumor biomarkers. These models were evaluated using root-mean-squared-error (RMSE) and the Spearman correlation coefficient.

Results

A total of 743 radiomics features were extracted from T1w and T2w MRI data separately. Subsequently, a feature selection process was conducted to identify a subset of five features for modeling. For therapeutic prediction, four classification models were developed. Support vector machine (SVM) model, utilizing combined T1w + T2w MRI data, achieved 96% accuracy and an AUROC of 1.00 in differentiating the control and treatment groups. For multi-class treatment outcome prediction, Linear regression model attained 85% accuracy and an AUC of 0.93. Histological analysis showed that combination therapy of NK cell and sorafenib had the lowest tumor cell viability and the highest NK cell activity. Correlation analyses between MRI features and histological biomarkers indicated robust relationships (r = 0.94).

Conclusions

Our study underscored the significant potential of texture-based MRI imaging features in the early assessment of multiple HCC treatment outcomes.

Sorafenib, FDA-approved therapy for patients with advanced hepatocellular carcinoma (HCC), leads to limited improvement in overall survival. However, it may indirectly impact the expansion and activity of natural killer (NK) cells. While NK cell-based immunotherapies generally exhibit favorable safety profiles, their effectiveness in controlling solid tumor growth is constrained, primarily due to the absence of antigen specificity and suboptimal expansion and persistence within the tumor microenvironment. In this study, we postulated that enhancing NK cell functionality via cytokine activation could bolster their viability and cytotoxic capabilities, leading to an improved therapeutic response when combined with sorafenib. Memory-like (ML)-NK cells were generated through the supplementation of optimal concentrations of interleukin (IL)-12 and IL-18 cytokines. Following a single day of treatment, cytotoxicity against rat and human HCC cells was evaluated via flow cytometry analysis. A rat HCC model was developed in 30 animals via subcapsular implantation and assigned to control, NK, sorafenib, ML-NK, and combination groups. Sorafenib was administered orally, and NK cells were delivered via the intrahepatic artery. Tumor growth was measured one week after treatment evaluation. Therapeutic efficacy during in-vitro and in-vivo analysis was investigated through a one-way ANOVA test, followed by pairwise two-tailed Student t-tests, considering P < 0.05 statistically significant. The in-vitro experiment results demonstrated that sorafenib and conventional NK cell therapies induced more substantial cell death than the control group (P < 0.01). ML NK cells significantly improved cell death compared to conventional NK cell immunotherapy. Furthermore, sorafenib in combination with ML-NK cells significantly decreased the viability of HCC cells (P < 0.05) compared to sorafenib plus conventional NK cell combination therapy. In vivo experiments have shown that sorafenib and ML-NK cell immunotherapy reduced the growth rate of HCC tumors compared to conventional NK immunotherapy and control groups. Notably, a combination of sorafenib and ML-NK cell immunochemotherapy resulted in the most significant suppression of tumor growth when compared to other therapies. In conclusion, our experimental findings demonstrate that the concurrent administration of sorafenib and ML-NK immunotherapy enhances cytotoxicity against HCC by optimizing the therapeutic response through cytokine activation, resulting in a significant decrease in tumor growth.

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the challenges and potential of using immunotherapy to treat pancreatic cancer, highlighting the role of artificial intelligence (AI) as a promising tool to enhance early detection and monitor the effectiveness of these therapies. By synthesizing recent advancements and identifying gaps in the current research, this review aims to provide a comprehensive overview of how AI and immunotherapy can be integrated to develop more personalized and effective treatment strategies. The insights from this review may guide future research efforts and contribute to improving patient outcomes in pancreatic cancer management.