Good news! Cancer is history (soon)!
Again Nature journal uses the horrible ideological term "people" instead of women!!!
triple-negative breast cancer (TNBC)
"A new ‘virtual cell model’ could help to choose more personalized treatments for women people with hard-to-treat triple-negative breast cancer. Researchers trained the prototype artificial-intelligence-based model, called ProteinTalks, on millions of protein measurements collected over time from breast cancer cell lines that were treated with dozens of antitumour drugs. The model did a good job of predicting how cells would respond to drugs it hadn’t seen before and also real-world clinical outcomes. “This is the first time that a virtual cell model goes out of the laboratory and is tested in a clinical scenario,” ...
In experiments using biopsies taken from individuals with TNBC, the virtual cell model showed promising results in identifying the same drugs that would prove to be effective when given to women people. The authors say this raises the possibility of more personalized care for TNBC, which accounts for 15–20% of breast cancer cases. ...
In their study, Guo and his colleagues aimed to develop a virtual cell model of breast cancer cells using proteomics data. They trained an AI model on more than 38 million protein measurements collected from 18 breast cancer cell lines, 16 of which were TNBC cells. The researchers treated the cells with 63 antitumour drugs that have been approved by the US Food and Drug Administration, and 59 drug combinations. ..."
From the abstract:
"Artificial intelligence-empowered virtual cell models represent an emerging approach for in silico drug discovery, yet most existing approaches lack large-scale, time-resolved perturbation proteomics data and interpretable frameworks for predicting therapeutic responses.
Here we generated more than 38 million temporal protein-abundance measurements from systematically perturbed breast cancer cell lines, and developed ProteinTalks, a virtual cell model.
Central to ProteinTalks is the synergy of this large-scale dynamic proteomic resource and the model architecture, enabling a new pretraining framework that learns transferable dynamical latent representations from temporal proteome trajectories. By modelling how proteins respond conditionally to different perturbations, this approach enables the model to function as an operational tool for diverse drug discovery tasks: predicting drug efficacy and synergy, discovering new drug combinations, probing proteins associated with drug resistance, stratifying patient responses and prioritizing drug candidates for patient organoids.
It also shows robust transferability, extending beyond cell lines to patient-derived organoids and clinical biopsies, generally achieving higher performance than the selected benchmark implementations under the evaluated protocols.
Together, ProteinTalks shows how scalable pretraining of transferable dynamic representations enables operational, dynamics-aware, proteomics-based virtual cell models to advance in silico drug discovery."
AI model predicts which breast-cancer drugs work best (behind paywall) "Model trained on millions of protein measurements can gauge drug effectiveness in tissue samples taken from people with triple-negative breast cancer."
An operational perturbation proteomics-based virtual cell model (no public access, but article above contains link to PDF)
A perturbation proteomics-based foundation model for virtual cell construction (preprint, open access)
Fig. 1 Overview of this perturbation proteomics study.
Fig. 3 Development and performance of the ProteinTalks model.
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