使用生物学知情的可见神经网络的扩散大型B细胞淋巴瘤患者的可解释的生存模型
An interpretable survival model for diffuse large B-cell lymphoma patients using a biologically informed visible neural network
影响因子:4.10000
分区:生物学3区 / 生化与分子生物学3区
发表日期:2024 Dec
作者:
Jie Tan, Jiancong Xie, Jiarong Huang, Weizhen Deng, Hua Chai, Yuedong Yang
摘要
弥漫性大B细胞淋巴瘤（DLBCL）是非霍奇金淋巴瘤（NHL）最常见的亚型，其特征是高异质性。评估其预后和遗传亚型具有显着的临床意义。但是，现有的DLBCL预后模型主要基于转录组谱，而遗传变异检测更常用于临床实践中。此外，当前基于聚类的亚型方法主要集中在具有高突变频率的基因上，这为DLBCL的异质性提供了足够的解释。在这里，我们提出了VNNSURV（https://bio-web1.nscc-gz.cn/app/vnnsurv），这是基于生物知情的可见神经网络（VNN）的DLBCL患者的生存模型。 VNNSURV在交叉验证集（HMRN队列，n = 928）上达到了平均C索引为0.72，表现优于基线方法。 VNNSURV的显着解释性促进了对最有影响力的基因以及它们对患者结局作用的基本途径的识别。当仅将30个最高影响基因用作遗传输入时，VNNSURV的总体性能得到了改善，并且在外部TCGA队列上达到了0.70的C-指数（n = 48）。利用这些高影响力基因，包括16个具有较低（<5％）变化频率的基因，我们设计了一种基于遗传的预后指数（GPI），用于风险分层和亚型鉴定方法。我们根据国际预后指数（IPI）将患者组分为三个风险等级，并具有很大的预后差异。此外，定义的亚型比基于聚类的方法表现出更大的预后一致性。从广义上讲，VNNSURV是一个有价值的DLBCL生存模型。它的高解释性对于精确药物具有重要的价值，并且其框架可扩展到其他疾病。
Abstract
Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and is characterized by high heterogeneity. Assessment of its prognosis and genetic subtyping hold significant clinical implications. However, existing DLBCL prognostic models are mainly based on transcriptomic profiles, while genetic variation detection is more commonly used in clinical practice. In addition, current clustering-based subtyping methods mostly focus on genes with high mutation frequencies, providing insufficient explanations for the heterogeneity of DLBCL. Here, we proposed VNNSurv (https://bio-web1.nscc-gz.cn/app/VNNSurv), a survival model for DLBCL patients based on a biologically informed visible neural network (VNN). VNNSurv achieved an average C-index of 0.72 on the cross-validation set (HMRN cohort, n = 928), outperforming the baseline methods. The remarkable interpretability of VNNSurv facilitated the identification of the most impactful genes and the underlying pathways through which they act on patient outcomes. When only the 30 highest-impact genes were used as genetic input, the overall performance of VNNSurv improved, and a C-index of 0.70 was achieved on the external TCGA cohort (n = 48). Leveraging these high-impact genes, including 16 genes with low (<5 %) alteration frequencies, we devised a genetic-based prognostic index (GPI) for risk stratification and a subtype identification method. We stratified the patient group according to the International Prognostic Index (IPI) into three risk grades with significant prognostic differences. Furthermore, the defined subtypes exhibited greater prognostic consistency than clustering-based methods. Broadly, VNNSurv is a valuable DLBCL survival model. Its high interpretability has significant value for precision medicine, and its framework is scalable to other diseases.