基于多模态数据预测胃癌对抗HER2治疗或抗HER2联合免疫治疗的反应

Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data

Signal Transduction and Targeted Therapy

影响因子:52.7

分区:医学1区 Top / 生化与分子生物学1区细胞生物学1区

发表日期:2024 Aug 26

作者: Zifan Chen, Yang Chen, Yu Sun, Lei Tang, Li Zhang, Yajie Hu, Meng He, Zhiwei Li, Siyuan Cheng, Jiajia Yuan, Zhenghang Wang, Yakun Wang, Jie Zhao, Jifang Gong, Liying Zhao, Baoshan Cao, Guoxin Li, Xiaotian Zhang, Bin Dong, Lin Shen

DOI: 10.1038/s41392-024-01932-y

摘要

单一模态数据的应用往往无法全面捕捉患者之间的复杂异质性，包括对抗HER2治疗的耐药性变异以及联合治疗方案的疗效差异，尤其是在HER2阳性胃癌（GC）治疗中。这一模态不足在许多研究中未被充分考虑。此外，人工智能在预测治疗反应，特别是在复杂疾病如GC中的应用仍处于起步阶段。因此，本研究旨在采用综合分析方法，准确预测HER2阳性胃癌患者对抗HER2治疗或抗HER2联合免疫治疗的反应。我们收集了包括放射学、病理学和临床信息的多模态数据，来自一队共429名患者：其中310名接受抗HER2治疗，119名接受抗HER2联合抗PD-1/PD-L1免疫治疗。我们引入了一种名为多模态模型（MuMo）的深度学习模型，将这些数据整合以实现精准的治疗反应预测。MuMo在抗HER2治疗中达到0.821的曲线下面积（AUC），在联合免疫治疗中达到0.914。此外，MuMo判断为低风险的患者显示出显著延长的无进展生存期和总生存期（log-rank检验，P < 0.05）。这些发现不仅强调了多模态数据分析在提升HER2阳性胃癌治疗评估和个体化医疗中的重要性，也展示了我们模型的潜力和临床价值。

Abstract

The sole use of single modality data often fails to capture the complex heterogeneity among patients, including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens, for the treatment of HER2-positive gastric cancer (GC). This modality deficit has not been fully considered in many studies. Furthermore, the application of artificial intelligence in predicting the treatment response, particularly in complex diseases such as GC, is still in its infancy. Therefore, this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive GC. We collected multi-modal data, comprising radiology, pathology, and clinical information from a cohort of 429 patients: 310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors immunotherapy. We introduced a deep learning model, called the Multi-Modal model (MuMo), that integrates these data to make precise treatment response predictions. MuMo achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined immunotherapy. Moreover, patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival (log-rank test, P < 0.05). These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer, but also the potential and clinical value of our model.