Intelligent Systems Science and Engineering College, Harbin Engineering University, Harbin 150001, China.
Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China.
Brief Bioinform. 2024 Mar 27;25(3). doi: 10.1093/bib/bbae104.
Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.
嵌合抗原受体 T 细胞（CAR-T）免疫疗法是一种治疗血液癌的新方法，与细胞因子释放综合征（CRS）的产生有关，这给患者的安全带来了重大风险。目前，对于与 CRS 相关的细胞因子以及细胞因子与细胞之间的复杂关系，我们的了解有限。因此，探索一种可靠且高效的计算方法来识别与 CRS 相关的细胞因子是至关重要的。在本研究中，我们提出了 Meta-DHGNN，这是一种基于元学习的有向异质图神经网络分析方法。所提出的方法整合了有向和异质算法，而元学习模块有效地解决了数据有限的问题。这种方法能够全面分析细胞因子网络，并准确预测与 CRS 相关的细胞因子。首先，为了解决小数据集带来的挑战，我们使用元学习模块进行了预训练阶段。因此，有向算法构建了一个邻接矩阵，更真实地捕捉潜在的关系。最终，异质算法使用元图和多头注意力机制来增强预测与正标签相关的细胞因子信息的现实性和准确性。我们在数据集上的实验验证表明，Meta-DHGNN 取得了良好的结果。此外，基于预测结果，我们从多个角度探讨了 CAR-T 治疗中 CRS 的多方面形成机制，并确定了一些细胞因子，如 IFNG（IFN-γ）、IFNA1、IFNB1、IFNA13、IFNA2、IFNAR1、IFNAR2、IFNGR1 和 IFNGR2，这些细胞因子在之前的研究中相对被忽视，但可能在其中发挥关键作用。Meta-DHGNN 的意义在于它能够有效地分析生物学中的有向异质网络，同时促进 CAR-T 治疗中 CRS 风险预测。
