Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies
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Abstract
Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted "driver" signaling pathways were pharmacologically targeted in these cell line models as proof of…
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7Topics & keywords
Topics
Keywords
- Triple-negative breast cancer
- Breast cancer
- Identification (biology)
- Medicine
- Selection (genetic algorithm)
- Cancer
- Triple negative
- Computational biology
UN Sustainable Development Goals
- Good health and well-being
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