Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer

It is well-established that interpatient heterogeneity in cancer may significantly affect genomic data analyses and in particular, network topologies. Most existing graphical model methods estimate a single population-level graph for genomic or proteomic network. In many investigations, these networks depend on patient-specific indicators that characterize the heterogeneity of individual networks across subjects with respect to subject-level covariates. Examples include assessments of how the network varies with patient-specific prognostic scores or comparisons of tumor and normal graphs while accounting for tumor purity as a continuous predictor. In this paper, we propose a novel edge regression model for undirected graphs, which estimates conditional dependencies as a function of subject-level covariates. We evaluate our model performance through simulation studies focused on comparing tumor and normal graphs while adjusting for tumor purity. In application to a dataset of proteomic measurements on plasma samples from patients with hepatocellular carcinoma (HCC), we ascertain how blood protein networks vary with disease severity, as measured by HepatoScore, a novel biomarker signature measuring disease severity. Our case study shows that the network connectivity increases with HepatoScore and a set of hub genes as well as important gene connections are identified under different HepatoScore, which may provide important biological insights to the development of precision therapies for HCC.

学界普遍认为，癌症患者间的异质性可显著影响基因组数据分析，尤其是网络拓扑结构（network topologies）。现有绝大多数图模型方法仅针对基因组或蛋白质组网络（proteomic network）估计单一的群体水平网络。在诸多研究中，此类网络依赖于患者特异性指标，用以刻画不同受试者的个体网络基于受试者水平协变量（subject-level covariates）的异质性。相关实例包括：评估网络如何随患者特异性预后评分变化，或是在将肿瘤纯度作为连续预测变量的前提下，对比肿瘤与正常组织的网络图谱。
本文提出一种针对无向图的新型边回归模型，该模型可将条件依赖关系建模为受试者水平协变量的函数。我们通过聚焦于校正肿瘤纯度后对比肿瘤与正常组织网络图谱的模拟实验，对所提模型的性能进行了评估。
将所提模型应用于肝细胞癌（hepatocellular carcinoma, HCC）患者血浆样本的蛋白质组测量数据集后，我们明确了血液蛋白质网络如何随疾病严重程度变化——该严重程度由新型生物标志物特征HepatoScore量化。本案例研究表明，网络连通性随HepatoScore升高而增强，且在不同HepatoScore水平下可识别出一系列核心基因（hub genes）与重要基因关联，这可为肝细胞癌精准治疗的研发提供关键生物学见解。