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Updated: Nov 7, 2025
Author Spotlight: Exploring Salidroside's Molecular Mechanisms in Breast Cancer Treatment
Published on: June 9, 2023
Deciphering the signaling network of breast cancer improves drug sensitivity prediction
Marco Tognetti1, Attila Gabor2, Mi Yang3
1Department of Quantitative Biomedicine, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Life Sciences, University of Zürich, 8057 Zurich, Switzerland; Institute of Molecular Systems Biology, ETH Zürich, 8093 Zurich, Switzerland; Molecular Life Science PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8057 Zurich, Switzerland.
Abstract:
One goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.
Insights
Precision medicine uses single-cell signaling data to predict cancer drug sensitivity. This approach identifies genomic markers, like DDIT3 mutations, to guide tailored breast cancer treatments.
Area of Science:
- Systems Biology
- Cancer Research
- Precision Medicine
Background:
- Precision medicine aims to personalize cancer treatments based on molecular profiles.
- Understanding single-cell signaling is crucial for predicting treatment responses.
Purpose of the Study:
- To characterize single-cell signaling landscapes in breast cancer cell lines.
- To develop mechanistic models for predicting drug sensitivity.
- To identify genomic features associated with drug response.
Main Methods:
- Mass cytometry was used to analyze 34 markers in over 80 million single cells across 62 breast cancer cell lines and 5 healthy lines.
- Cells were stimulated with epidermal growth factor (EGF) and treated with five kinase inhibitors.
- Mechanistic signaling network models were fitted to the single-cell data.
Main Results:
- The models accurately predicted drug sensitivity.
- Genomic features, including a DDIT3 missense mutation, were identified as predictive of sensitivity to PI3K inhibitors.
- Similar genotype-drug sensitivity trends were observed in patient-derived xenograft models.
Conclusions:
- Patient-specific single-cell measurements and modeling can inform precision medicine strategies.
- This approach offers a proof of principle for developing targeted cancer therapies.
- Mechanistic models provide insights into cancer cell information processing and drug response.
