AI tool to evaluate metabolic flux in patients with brain cancer
Quantifying metabolic activity in patient tumors could advance personalized cancer targeting.
The researchers develop a digital twin framework using machine learning to quantify metabolic fluxes in tissues from patients with glioma.
First, the digital twin framework (DTF) to estimate fluxes in patient bulk samples and the second, the single-cell metabolic flux analysis allows single-cell-level flux quantification.
The technique allowed quantification of metabolic activity in neoplastic glioma cells, and revealed frequently elevated purine synthesis and serine uptake, compared with non-malignant cells.
Thus, the technology may help to identify which patients may benefit from different targeted metabolic therapies like specialized diets or pharmacologic agents.
https://www.cell.com/cell-metabolism/fulltext/S1550-4131(25)00482-6
