Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction
Huajie Song,
Xiaowei Tang,
Miao Liu,
Guangxi Wang,
Yuyao Yuan,
Ruifang Pang,
Chenyi Wang,
Juntuo Zhou,
Yang Yang,
Mengmeng Zhang,
Yan Jin,
Kewei Jiang,
Shu Wang,
Yuxin Yin
Affiliations
Huajie Song
Department of Pathology, Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
Xiaowei Tang
Department of Pathology, Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
Miao Liu
Breast Center, Peking University People’s Hospital, Beijing 100044, China
Guangxi Wang
Department of Pathology, Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
Yuyao Yuan
Department of Pathology, Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
Ruifang Pang
Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen 518036, P.R. China
Chenyi Wang
Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing 100044, China
Juntuo Zhou
Department of Pathology, Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
Yang Yang
Breast Center, Peking University People’s Hospital, Beijing 100044, China
Mengmeng Zhang
Breast Center, Peking University People’s Hospital, Beijing 100044, China
Yan Jin
Department of Pathology, Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
Kewei Jiang
Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing 100044, China
Shu Wang
Breast Center, Peking University People’s Hospital, Beijing 100044, China; Corresponding author
Yuxin Yin
Department of Pathology, Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China; Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen 518036, P.R. China; Corresponding author
Summary: 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 the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the training, test, and independent validation cohorts. Inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in patients with TNBC. This study deepens our understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive method for early breast cancer detection and predicting NAC response in patients with TNBC.
