A “revolutionary” artificial intelligence technology is able to monitor the response of cancer cells to new drugs, British developers have announced.
The AI system observes changes to the shapes of cells in three dimensions and is already able to identify which drugs are acting on the cells through this means, according to the researchers.
The developers say it accurately identifies underlying biochemical changes when cancer cells are treated with specific drugs. It was also able to identify significant proteins which could be targeted by new drugs.
The key breakthrough in the technology is that it analyses cell shape in 3D, rather than the 2D shape offered by microscopy of cells on a flat surface. It can also take into account the variability in a group of cells, rather than having to calculate an average cell shape.
The tool was developed at the Institute of Cancer Research (ICR) in London. It was developed using melanoma cells, but the researchers say they have already shown it works for red blood cells and stem cells.
The researchers hope the technology will significantly speed up drug development. It is now being deployed on research into targeted protein degraders.
Professor Chris Bakal, professor of cancer morphodynamics, said: “3D cell shape is like a fingerprint of cellular state and function – it’s a previously untapped reservoir of information. Using AI, we can decode this fingerprint and reveal how cells respond to drugs.
“The tool that we’ve created is so powerful that we will be able to streamline the years-long drug discovery process, saving both time and money. Patients with cancer need new treatment options as quickly as possible, so speeding up this process will be hugely valuable.”
ICR chief executive Professor Kristian Helin said: “This latest technology builds on years of work at the ICR to understand cancer cell shape and to use artificial intelligence to analyse data. I look forward to seeing this technology being used to develop new medicines that have a real impact for people with cancer.”
Source:
De Vries M, Dent LG, Curry N, Rowe-Brown L, Bousgouni V, Fourkioti O, Naidoo R, Sparks H, Tyson A, Dunsby C, Bakal C. (2025) “Geometric deep learning and multiple-instance learning for 3D cell-shape profiling.” Cell Systems, 19 March 2025, doi: 10.1016/j.cels.2025.101229.
Link: https://www.cell.com/cell-systems/fulltext/S2405-4712(25)00062-6
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