GoKawiilWestlake University AI virtual-cell model predicts drug response in triple-negative breast cancer
Researchers at Westlake University built an AI-based 'virtual cell model' trained on over 38 million protein measurements from breast cancer cell lines, most of them triple-negative. Tested on real patient biopsies, the model successfully identified which drugs would be effective against individual tumours, matching actual clinical outcomes. The findings, published in Nature, mark the first time such a virtual cell model has moved from lab experiments to clinical testing.
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Triple-negative breast cancer lacks the hormone and growth-protein receptors that guide standard targeted therapies, making treatment selection largely trial-and-error. A tool that can predict drug response from a patient's own tumour proteomics could speed up personalized treatment decisions for a cancer subtype that affects 15-20% of breast cancer patients and is notoriously aggressive.
- The AI model uses proteomics rather than gene-sequencing data to simulate cancer cell behavior.
- It was validated against real biopsies from triple-negative breast cancer patients, not just cell lines.
- The approach is narrowly focused on drug response prediction rather than full cellular simulation.
Source: nature.com — Naddaf, 2026-09-09
Published there as: “AI model predicts which breast-cancer drugs work best”
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