An Integrative Model of Single Cell Transcriptomic States for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC), an aggressive and highly heterogeneous subtype of breast cancer, poses significant challenges for treatment due to its molecular diversity and resistance to standard therapies. Accounting for 10–20% of all breast cancer cases, TNBC lacks specific biological markers, making it difficult to classify and treat effectively. Traditional approaches based on bulk RNA sequencing obscure intratumoral heterogeneity and fail to capture distinct cellular states within tumors. In this study, we constructed a comprehensive single-cell transcriptomic map of TNBC by analyzing a cohort of published TNBC patient datasets, identifying nine transcriptomic states, or metaprograms, which capture the core behaviors of TNBC cells, including cancer stem cell properties, epithelial-to-mesenchymal transition (EMT), immune modulation, metabolic adaptation, and vasculogenic mimicry. We observed that these metaprograms are variably expressed within and across patient tumors, underscoring the complexity of TNBC. By integrating TNBC-specific metaprograms with established breast cancer subtypes, we found significant prognostic associations, with specific metaprograms correlating with poor survival outcomes. This study highlights the need for single-cell approaches to uncover TNBC’s molecular heterogeneity and suggests that metaprogram-based classification could facilitate more precise therapeutic interventions.