BackgroundTumor cell heterogeneity is a fundamental driver of breast cancer aggressiveness, underlying recurrence, metastasis, and therapy resistance. Understanding the biological characteristics and functions of specific tumor cell clusters in the tumor microenvironment is crucial for advancing precision oncology.MethodsWe delineated breast cancer tumor cell heterogeneity by integrating single-cell transcriptomics, spatial transcriptomics, bulk transcriptomics, genomic and radiomic data.
The oncogenic functions of the candidate gene TMSB10 were rigorously validated in vitro. To advance individualized patient management, we employed machine learning to develop a non-invasive MRI radiomic model for estimating tumor cluster abundance and a robust prognostic signature for risk stratification.ResultsWe discovered a poor-prognosis tumor cell cluster (C1 cluster).
C1 cluster exhibited a late evolutionary state, metabolic reprogramming (OXPHOS/glycolysis), and active crosstalk with cancer-associated fibroblasts and endothelial cells. High abundance of C1 cluster was associated with poor survival, specific somatic mutations, and predicted superior response to immune checkpoint blockade, but not to chemo/radiotherapy.
Frontiers in Immunology published a clinical update in Infectious Disease on 20 Apr 2026.
The item focuses on Integrative multi-omics and radiomics reveal a TMSB10-driven cell state for non-invasive assessment and precision stratification in breast cancer.
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