Survival prognosis in advanced HER-2 negative gastric cancer treated with immunochemotherapy: A novel model

BACKGROUND: Gastric cancer is one of the most common malignant tumors of the digestive system globally, with a generally poor prognosis for patients with advanced disease. In recent years, immune checkpoint inhibitors have made significant advancements in gastric cancer treatment, with some HER-2 negative advanced gastric cancer patients benefiting from the combination of immunotherapy and chemotherapy. However, significant biological heterogeneity exists among patients, resulting in a lack of effective tools to predict the benefits of immunotherapy and survival outcomes. Therefore, there is an urgent need to develop a scientific and precise survival prediction model to provide robust support for personalized treatment decisions. AIM: To develop and validate a novel survival prediction model for assessing the survival risk of advanced HER-2 negative gastric cancer patients receiving immunotherapy combined with chemotherapy, thereby enhancing the accuracy of prognostic evaluation and its clinical guidance value. METHODS: This retrospective study included 200 advanced HER-2 negative gastric cancer patients who received programmed cell death protein 1 inhibitors combined with chemotherapy. Independent prognostic factors for progression-free survival (PFS) and overall survival (OS) were identified using multivariable Cox regression analysis, and a nomogram model was constructed based on these factors. The variables included in the regression analysis were selected based on their clinical relevance, routine application in gastric cancer evaluation, and availability within our dataset. The model's discrimination and calibration were assessed using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration plots. RESULTS: values < 0.05). Based on these variables, nomogram models for PFS and OS were constructed. In the training set, the C-index for the PFS model was 0.82 [95% confidence interval (CI): 0.77-0.87], and in the internal validation set, it was 0.78 (95%CI: 0.70-0.87), indicating good discrimination ability. For AUC evaluation, the PFS model's 3-month and 6-month prediction AUCs in the training set were 0.79 (95%CI: 0.65-0.92) and 0.89 (95%CI: 0.83-0.94), respectively. In the validation set, they were 0.82 (95%CI: 0.68-0.97) and 0.80 (95%CI: 0.68-0.92), respectively. For OS prediction, the C-index in the training set and validation set were 0.81 (95%CI: 0.76-0.86) and 0.78 (95%CI: 0.69-0.87), respectively. The nomogram also showed high accuracy in predicting OS at 12, 15, and 18 months. In the training set, the AUCs were 0.82 (95%CI: 0.75-0.89), 0.91 (95%CI: 0.86-0.97), and 0.89 (95%CI: 0.83-0.95), respectively. In the validation set, they were 0.79 (95%CI: 0.66-0.91), 0.84 (95%CI: 0.73-0.96), and 0.81 (95%CI: 0.69-0.93), respectively. Furthermore, calibration curves demonstrated that the predicted probabilities of the model were highly consistent with the actual observed values at different time points, suggesting that the model has good reliability and adaptability for clinical application. CONCLUSION: The nomogram model developed in this study effectively predicts the survival outcomes of advanced HER-2 negative gastric cancer patients receiving immunotherapy combined with chemotherapy, demonstrating good discrimination and consistency, and providing robust support for personalized clinical treatment decisions.