使用Mapper算法对乳腺癌患者进行拓扑信息生存分析
Topology-Informed Survival Analysis of Breast Cancer Patients Using the Mapper Algorithm
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中文总结 AI 辅助
该研究运用Mapper算法分析超千名乳腺癌患者基因表达数据，识别与生存相关分子模式，发现网络高风险区患者生存差、高增殖模式与不良结果有关，还找到生存不符预期的患者，验证拓扑衍生风险组含预后信息。
中文摘要 AI 辅助
本研究将拓扑数据分析（TDA）中的数学工具Mapper算法应用于1000多名TCGA-BRCA患者的基因表达数据，以识别与生存相关的隐藏分子模式。网络高风险区域附近的患者生存明显较差，高增殖基因表达模式总体上与较差结果相关，尽管治疗缩小了增殖组间的生存差距。分析还发现了生存结果与预期临床行为不符的患者，包括一组基底样患者有意外良好结果，与独特、更易治疗的基因特征有关，揭示了传统分类方法遗漏的分子程序。通过对未见过患者的训练和测试验证，调整年龄、肿瘤分期和治疗后，拓扑衍生风险组仍与生存显著相关，表明基因表达数据的几何结构包含超越传统乳腺癌分类方法的临床有意义的预后信息。
英文摘要
This study applied a mathematical tool from Topological Data Analysis (TDA), called the Mapper algorithm, to gene expression data from more than 1,000 TCGA-BRCA patients to identify hidden molecular patterns associated with survival. Patients located near high-risk regions of the network showed significantly poorer survival, and highly proliferative gene expression patterns were associated with worse outcomes overall, although treatment narrowed this survival gap across proliferation groups. The analysis further uncovered patients whose survival outcomes were inconsistent with their expected clinical behavior, including a subgroup of Basal-like patients with unexpectedly favorable outcomes linked to a distinct, more treatment-responsive gene signature, revealing molecular programs missed by traditional classification methods. Validation through training and testing on unseen patients confirmed that topology-derived risk groups remained significantly associated with survival after adjusting for age, tumor stage, and treatment, demonstrating that the geometric structure of gene expression data contains clinically meaningful prognostic information beyond traditional breast cancer classification methods.
