Additional file 2 of Construction of a risk stratification model integrating ctDNA to predict response and survival in neoadjuvant-treated breast cancer

Additional file 2: sFig. 1. Comparing the differences in the performance of predicting pCR with different clinical factors, SNV and CNV characters. sFig. 2. (A) Sankey plot showing the differences in patients ctDNA cleared at different time points (T1, T2, T3). (B) Sankey plot showing the differences in patients with positive ctDNA at different time points (T1, T2, T3). sFig. 3. Kaplan–Meier analysis of DFS stratified based on ctDNA status during NAC. sFig 4. Overall algorithm flowchart for the predictive model construction.

附加文件2：
sFig. 1. 对比不同临床因素、单核苷酸变异（Single Nucleotide Variant, SNV）与拷贝数变异（Copy Number Variant, CNV）特征在预测病理完全缓解（pathologic complete response, pCR）中的性能差异。
sFig. 2. (A) 桑基图展示不同时间节点（T1、T2、T3）患者循环肿瘤DNA（circulating tumor DNA, ctDNA）的清除状态差异；(B) 桑基图展示不同时间节点（T1、T2、T3）患者循环肿瘤DNA（circulating tumor DNA, ctDNA）阳性情况的差异。
sFig. 3. 基于新辅助化疗（Neoadjuvant Chemotherapy, NAC）期间循环肿瘤DNA（circulating tumor DNA, ctDNA）状态分层的无病生存期（Disease-Free Survival, DFS）Kaplan–Meier分析。
sFig. 4. 预测模型构建的完整算法流程图。