Table3_Identification of Pathway-Based Biomarkers with Crosstalk Analysis for Overall Survival Risk Prediction in Breast Cancer.XLS

Recently, many studies have investigated the role of gene-signature on the prognostic assessment of breast cancer (BC), however, the tumor heterogeneity and sequencing noise have limited the clinical usage of these models. Pathway-based approaches are more stable to the perturbation of certain gene expression. In this study, we constructed a prognostic classifier based on survival-related pathway crosstalk analysis. We estimated pathway’s deregulation scores (PDSs) for samples collected from public databases to select survival-related pathways. After pathway crosstalk analysis, we conducted K-means clustering analysis to cluster the patients into G1 and G2 subgroups. The survival outcome of the G2 subgroup was significantly worse than the G1 subgroup. Internal and external dataset exhibits high consistency with the training dataset. Significant differences were found between G2 and G1 subgroups on pathway activity, gene mutation, immune cell infiltration levels, and in particular immune cells/pathway’s activities were significantly negatively associated with BC patient’s outcomes. In conclusion, we established a novel classifier reflecting the overall survival risk of BC and successfully validated its clinical usage on multiple BC datasets, which could offer clinicians inspiration in formulating the clinical treatment plan.

近年来，多项研究探讨了基因特征（gene-signature）在乳腺癌（breast cancer, BC）预后评估中的作用，但肿瘤异质性与测序噪声严重限制了此类模型的临床应用。基于通路（pathway）的分析方法对特定基因表达扰动的稳定性更佳。本研究基于生存相关通路串扰分析构建了一款预后分类器（prognostic classifier）。我们对公共数据库获取的样本计算了通路失调评分（pathway deregulation scores, PDSs），以此筛选生存相关通路。完成通路串扰分析后，我们实施K-means聚类分析将患者分为G1与G2两个亚组。G2亚组患者的生存结局显著劣于G1亚组。内部与外部数据集均与训练数据集具有高度一致性。G1与G2亚组在通路活性、基因突变及免疫细胞浸润水平上均存在显著差异，其中免疫细胞/通路活性与乳腺癌患者的临床结局呈显著负相关。综上，本研究构建了一款可反映乳腺癌患者总体生存风险的新型分类器，并在多组乳腺癌数据集上成功验证了其临床应用价值，可为临床医师制定临床治疗方案提供参考思路。