AI virtual cell model predicts effective drugs for triple‑negative breast cancer
Triple‑negative breast cancer (TNBC) lacks hormone receptors, making treatment difficult. Researchers at Westlake University created a virtual cell model that uses proteomics data to predict how individual tumour cells respond to drugs. The model was trained on 38 million protein measurements from 18 cell lines, including 16 TNBC lines, and tested on 63 FDA‑approved drugs and 59 combinations. It…
Key points
- Model trained on 38M protein measurements from 18 cell lines, 16 TNBC, predicting drug response with 88% accuracy
- Validated on 501 patient biopsies, matching real‑world treatment outcomes
- First virtual cell model tested clinically, enabling personalized TNBC therapy
This is the first time a virtual cell model has been validated in a real‑world clinical setting, offering a path toward personalized therapy for the 15–20 % of breast‑cancer patients with TNBC. By pinpointing effective drugs early, the approach could reduce trial‑and‑error treatment cycles and improve survival rates.
The study demonstrates how large‑scale proteomics and time‑course data can power AI to guide precision oncology, potentially reshaping how aggressive cancers are managed.
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