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Department of Quantitative Biomedicine

The Bodenmiller group shows that mapping the breast cancer signaling network improves drug sensitivity prediction

Deciphering the signaling network of breast cancer improves drug sensitivity prediction

Graphical Abstract © The Authors

This work provides proof of principle that pesonalized single-cell measurements combined with mechanistic modeling could guide effective precision medicine strategies. Using mass cytometry, the Bodenmiller group characterised signaling landscapes in 62 breast cancer cell lines and five healthy tissue cell lines. Data from over 80 million single cells under 4,000 conditions were used to build cell line-specific signaling network models that both accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, as validated in patient-derived xenograft mouse models. See Tognetti et al., Cell Systems