使用Mapper算法的拓扑信息驱动的乳腺癌患者生存分析

摘要

本研究应用拓扑数据分析（TDA）中的数学工具——Mapper算法，对超过1000例TCGA-BRCA患者的基因表达数据进行分析，以识别与生存相关的隐藏分子模式。位于网络高风险区域附近的患者生存率显著较低，高增殖性基因表达模式总体上与更差预后相关，但治疗缩小了不同增殖组之间的生存差距。分析进一步发现了部分患者其生存结局与预期临床行为不一致的情况，包括一组基底样亚型患者表现出出乎意料的良好预后，这与一种独特且对治疗更敏感的基因特征相关，揭示了传统分类方法未能识别的分子机制。通过对未参与模型构建的患者进行训练与测试的验证表明，在调整年龄、肿瘤分期和治疗因素后，基于拓扑结构的风险分组仍与生存显著相关，这证实了基因表达数据的几何结构蕴含超越传统乳腺癌分类方法的临床预后信息。

English

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.

使用Mapper算法的拓扑信息驱动的乳腺癌患者生存分析

Topology-Informed Survival Analysis of Breast Cancer Patients Using the Mapper Algorithm

摘要

Support