Kim TH, et al. J Gastric Cancer. 2023 Jan;23:e11. https://doi.org/10.5230/jgc.2023.23.e11

Kim IH, et al. J Gastric Cancer. 2025 Jan;25:e11. https://doi.org/10.5230/jgc.2025.25.e11

PURPOSE: Trastuzumab-containing chemotherapy is the recommended first-line regimen for human epidermal growth factor receptor 2 (HER2)–positive advanced gastric or gastroesophageal junction (G/GEJ) cancer. We evaluated the safety and efficacy of trastuzumab combined with ramucirumab and paclitaxel as second-line treatment for HER2-positive G/GEJ cancer. PATIENTS AND METHODS: Patients with HER2-positive advanced G/GEJ cancer who progressed after first-line treatment with trastuzumab-containing chemotherapy were enrolled from five centers in the Republic of Korea. Patients were administered a 28-day cycle of trastuzumab (once on days 1, 8, 15, and 22: 2 mg/kg followed by 4 mg/kg loading dose), ramucirumab (once on days 1 and 15: 8 mg/kg), and paclitaxel (once on days 1, 8, and 15: dose level 1, 80 mg/m²; or dose level -1, 70 mg/m²). Phase II was conducted with the recommended phase II dose (RP2D). Primary end points were determination of RP2D during phase Ib and investigator-assessed progression-free survival (PFS) in patients treated with RP2D. RESULTS: Dose-limiting toxicity at dose level 1 was not documented during phase Ib, and a full dose combination was selected as the RP2D. Among 50 patients with a median follow-up duration of 27.5 months (95% CI, 17.4 to 37.6), median PFS and overall survival were 7.1 months (95% CI, 4.8 to 9.4) and 13.6 months (95% CI, 9.4 to 17.7), respectively. Objective response rate was 54% (27 of 50, including one complete response), and disease control rate was 96% (48 of 50). Loss of HER2 expression was observed in 34.8% (8 of 23) patients after first-line treatment, and no definite association between HER2 expression and the outcome was revealed. Safety profiles were consistent with previous reports. CONCLUSION: Trastuzumab combined with ramucirumab and paclitaxel showed appreciable efficacy with manageable safety profiles in patients with previously treated HER2-positive G/GEJ cancer.

Aberrant expression of mucins (MUCs) can promote the epithelial–mesenchymal transition (EMT), which leads to enhanced tumorigenesis. Carcinogenesis-related pathways involving c-MET and β-catenin are associated with MUCs. In this study, we characterized the expression of EMT-relevant proteins including MET, β-catenin, and E-cadherin in human gastric cancer (GC) cell lines, and further characterized the differential susceptibility of these cell lines compared with the c-MET inhibitor tepotinib. We assessed the antitumor activity of tepotinib in GC cell lines. The effects of tepotinib on cell viability, apoptotic cell death, EMT, and c-MET and β-catenin signaling were evaluated by 3-(4,5 dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl-2-(4-sulfophenyl)-2H-tetrazolium (MTS), flow cytometry, Western blotting, and qRT-PCR. The antitumor efficacy was assessed in MKN45 xenograft mice. Tepotinib treatment induced apoptosis in c-MET-amplified SNU620, MKN45, and KATO III cells, but had no effect on c-MET-reduced MKN28 or AGS cells. Tepotinib treatment also significantly reduced the protein levels of phosphorylated and total c-MET, phosphorylated and total ERK, β-catenin, and c-MYC in SNU620 and MKN45 cells. In contrast, this drug was only slightly active against KATO III cells. Notably, tepotinib significantly reduced the expression of EMT-promoting genes such as MMP7, COX-2, WNT1, MUC5B, and c-MYC in c-MET-amplified GC cells and increased the expression of EMT-suppressing genes such as MUC5AC, MUC6, GSK3β, and E-cadherin. In a mouse model, tepotinib exhibited good antitumor growth activity along with increased E-cadherin and decreased phosphorylated c-MET (phospho-c-MET) protein levels. Collectively, these results suggest that tepotinib suppresses tumor growth and migration by negatively regulating c-MET-induced EMT. These findings provide new insights into the mechanism by which MUC5AC and MUC6 contribute to GC progression.

Tropomyosin receptor kinase (TRK) and receptor tyrosine kinase (RTK class VII) expression are important in many human diseases, especially cancers, including colorectal, lung, and gastric cancer. Using RNA sequencing analysis, we evaluated the mRNA expression and mutation profiles of gastric cancer patients with neurotropic tropomyosin receptor kinase (NTRK) 1-3 overexpression (defined as a ≥2.0-fold change). Furthermore, we screened eight TRK inhibitors in NCI-N87, SNU16, MKN28, MKN7, and AGS cells. Among these inhibitors, entrectinib showed the highest inhibitory activity; therefore, this drug was selected for analysis of its therapeutic mechanisms in gastric cancer. Entrectinib treatment induced apoptosis in NTRK1-3-expressing and VEGFR2-expressing NCI-N87 and AGS cells, but it had no effect on NTRK1-3-, VEGFR2-, TGFBR1-, and CD274-expressing MKN7 cells. SNU16 and MKN28 cells with low NTRK1-3 expression were not affected by entrectinib. Therefore, a mechanistic study was conducted in NCI-N87 (high expression of NTRK1-3 but mutation of NTRK3), AGS (high expression of NTRK1-3) and MKN28 (low expression of NTRK1-3) gastric cancer cell lines. Entrectinib treatment significantly reduced expression levels of phosphorylated NFκB, AKT, ERK, and β-catenin in NCI-N87 and AGS cells, whereas it upregulated the expression levels of ECAD in NCI-N87 cells. Together, these results suggest that entrectinib has anti-cancer activity not only in GC cells overexpressing pan NTRK but also in VEGFR2 GC cells via the inhibition of the pan NTRK and VEGFR signaling pathways.

Background: Accurate detection of axillary lymph node (ALN) metastases in breast cancer is crucial for clinical staging and treatment planning. This study aims to develop a deep learning model using clinical implication-applied preprocessed computed tomography (CT) images to enhance the prediction of ALN metastasis in breast cancer patients. Methods: A total of 1128 axial CT images of ALN (538 malignant and 590 benign lymph nodes) were collected from 523 breast cancer patients who underwent preoperative CT scans between January 2012 and July 2022 at Hallym University Medical Center. To develop an optimal deep learning model for distinguishing metastatic ALN from benign ALN, a CT image preprocessing protocol with clinical implications and two different cropping methods (fixed size crop [FSC] method and adjustable square crop [ASC] method) were employed. The images were analyzed using three different convolutional neural network (CNN) architectures (ResNet, DenseNet, and EfficientNet). Ensemble methods involving and combining the selection of the two best-performing CNN architectures from each cropping method were applied to generate the final result. Results: For the two different cropping methods, DenseNet consistently outperformed ResNet and EfficientNet. The area under the receiver operating characteristic curve (AUROC) for DenseNet, using the FSC and ASC methods, was 0.934 and 0.939, respectively. The ensemble model, which combines the performance of the DenseNet121 architecture for both cropping methods, delivered outstanding results with an AUROC of 0.968, an accuracy of 0.938, a sensitivity of 0.980, and a specificity of 0.903. Furthermore, distinct trends observed in gradient-weighted class activation mapping images with the two cropping methods suggest that our deep learning model not only evaluates the lymph node itself, but also distinguishes subtler changes in lymph node margin and adjacent soft tissue, which often elude human interpretation. Conclusions: This research demonstrates the promising performance of a deep learning model in accurately detecting malignant ALNs in breast cancer patients using CT images. The integration of clinical considerations into image processing and the utilization of ensemble methods further improved diagnostic precision.