Multi-omic analysis identifies metabolic biomarkers for early detection of breast cancer and prediction of therapeutic responses

Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in breast tumor cells and activation of regulatory T cells (Tregs) in the tumor microenvironment, which show distinction in subtypes of triple-negative breast cancer (TNBC) and non-TNBC patients and likely to impact on prognosis of BC through A2AR-Treg pathway. Combining machine learning with absolute quantitative plasma metabolomics, we establish an effective diagnostic model for early-stage breast cancer, utilizing a four-metabolite panel including two nucleoside metabolites, inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across training, test, and independent validation cohorts. Surprisingly, inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in TNBC patients. This study deepens the understanding of nucleotide metabolism in development of breast cancer and introduces a promising non-invasive approach with low radiation exposure for early breast cancer detection and predicting NAC response in TNBC patients.