一种基于生物学信息的可视神经网络的弥漫大B细胞淋巴瘤患者生存模型
An interpretable survival model for diffuse large B-cell lymphoma patients using a biologically informed visible neural network
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影响因子:4.1
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
DOI:
10.1016/j.csbj.2024.07.019
摘要
弥漫性大B细胞淋巴瘤（DLBCL）是非霍奇金淋巴瘤（NHL）中最常见的亚型，具有高度异质性。其预后评估和遗传亚型划分具有重要的临床意义。然而，现有的DLBCL预后模型主要基于转录组信息，而遗传变异检测在临床实践中更为常用。此外，基于聚类的亚型划分方法多关注突变频率高的基因，未能充分解释DLBCL的异质性。在此，我们提出了VNNSurv（https://bio-web1.nscc-gz.cn/app/VNNSurv），一种基于生物学信息的可视神经网络（VNN）为基础的DLBCL患者生存模型。VNNSurv在交叉验证集（HMRN队列，n=928）中达到了平均C指数0.72，优于基线方法。其显著的可解释性帮助识别了影响最大的重要基因及其作用的基础通路。当仅使用影响力最大的30个基因作为遗传输入时，模型性能提升，在外部TCGA队列（n=48）中达到了C指数0.70。借助这些高影响力基因（包括16个突变频率低于5%的基因），我们设计了基于遗传的预后指数（GPI）用于风险分层和亚型识别。根据国际预后指数（IPI）对患者进行分层，分为三类风险组，差异具有显著预后意义。此外，所定义的亚型比基于聚类的方法具有更好的预后一致性。总之，VNNSurv是一个有价值的DLBCL生存模型，其高度可解释性对于精准医疗具有重要价值，其框架也可扩展到其他疾病。
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.