Zhangxiang Zhao: The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Tongzhu Jin: Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, China.
Bo Chen: Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Qi Dong: Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Mingyue Liu: Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Jiayu Guo: Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, China.
Xiaoying Song: Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, China.
Yawei Li: Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Tingting Chen: Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Huiming Han: Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Haihai Liang: The Sino-Russian Medical Research Center of Jinan University, The Institute of Chronic Disease of Jinan University, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Yunyan Gu: Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Identifying robust breast cancer subtypes will help to reveal the cancer heterogeneity. However, previous breast cancer subtypes were based on population-level quantitative gene expression, which is affected by batch effects and cannot be applied to individuals. We detected differential gene expression, genomic, and epigenomic alterations to identify driver differential expression at the individual level. The individual driver differential expression reflected the breast cancer patients' heterogeneity and revealed four subtypes. Mesenchymal subtype as the most aggressive subtype harbored deletion and downregulated expression of genes in chromosome 11q23 region. Specifically, silencing of the gene in 11q23 promoted the invasion and migration of breast cancer cells in vitro by the epithelial-mesenchymal transition. The immunologically hot subtype displayed an immune-hot microenvironment, including high T-cell infiltration and upregulated PD-1 and CTLA4. Luminal and genomic-unstable subtypes showed opposite macrophage polarization, which may be regulated by the ligand-receptor pairs of CD99. The integration of multi-omics data at the individual level provides a powerful framework for elucidating the heterogeneity of breast cancer.
