Abstract P2-03-18: Lymph node metastasis of breast cancer: subclonal selection for high lipid metabolism from the primary tumor

Abstract Background: Lymph node metastasis is the most prevalent site of breast cancer dissemination and significantly influences patient prognosis and survival. Accurate prediction of axillary lymph node metastasis (ALNM) is crucial for tailoring surgical and pharmacological treatments. Understanding the underlying characteristics of lymph node metastasis can improve risk prediction and treatment strategies. Methods: We integrated single-cell transcriptomic data from 13 pairs of primary breast tumors and their matched lymph node metastases, sourced from our institution and public databases. Epithelial cells were clustered to explore the characteristics, and inferCNV was applied to trace the evolutionary lineage of metastatic cells. We validated the association between LNM-signature and lymph node metastasis using TCGA breast cancer data. Results: Single-cell transcriptomics identified lymph node metastatic cells with heightened lipid metabolism, emphasizing fatty acid synthesis and membrane related metabolism. Immunohistochemistry of expanded clinical cohorts and a murine model corroborated the findings. Analysis of the inferCNV evolutionary tree revealed that the main ancestors of metastases in the primary tumor were clones exhibiting elevated lipid metabolism compared to others. This suggested that nodal metastasis was driven by subclonal selection rather than emerging adaptation. This association between primary tumors and lymph node metastases was further reinforced by bulk RNA-Seq data from breast cancer patients in the TCGA dataset. We generated a LNM-signature from the nodal metastasis associated cluster. The signature score increased with higher N stage and the area under the curve (AUC) for the signature score reached 0.65 (95%IC 0.61∼0.71) within patients with the same T stage. Conclusions: Our findings indicate that lymph node metastasis arises from subclonal selection for high lipid metabolism from primary tumors. The corresponding signature score is a valuable predictor of nodal metastasis risk. Citation Format: Yue Zhou, Zehao Wang, Zhishuang Gao, Xiaoting Chen, Bingqiu Xiu, Jingyan Xue, Jiong Wu. Lymph node metastasis of breast cancer: subclonal selection for high lipid metabolism from the primary tumor [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P2-03-18.