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Introduction Breast cancer is the most common malignancy in women worldwide, exhibiting high heterogeneity that complicates diagnosis, treatment, and prognosis. While somatic mutations stably reveal genetic characteristics of tumor cells, their application in breast cancer subtyping remains underexplored. Methods A total of 2,526 breast cancer patients from Memorial Sloan Kettering Cancer Center were classified into different tumor ecosystem subtypes (TESs) based on somatic mutation profiles using a network propagation algorithm. Results The prognosis of breast cancer patients in TES 1 was significantly better than that of those in TES 2. Immunological characterization further revealed that the tumor microenvironment contained significantly more tumor immune cells in TES 1 than in TES 2, and that TES 2 had lower response to immunotherapy but was more sensitive to chemotherapeutic agents. Moreover, our tumor ecosystem subtyping method effectively classified patients across 20 cancer cohorts with good generalization. Conclusion This study proposes a stable, reproducible, and clinically applicable subtyping strategy based on somatic mutation data for tumor ecosystem subtyping, which can be used to guide personalized treatment for breast cancer patients and promote the development of precision medicine.
Ding et al. (Thu,) studied this question.