New AI Method Forces Cancer Stem Cells Into Self-Destruction
Researchers have developed a new approach to destroying cancer stem cells.
Scientists at University of California San Diego have developed a new approach to destroying cancer stem cells – hard-to-find cells that help cancers spread, come back after treatment and resist therapy. The new approach, which the researchers tested in colon cancer, leveraged artificial intelligence (AI) to identify treatments that can reprogram cancer stem cells, ultimately triggering them to self-destruct. Because it only targets cancer cells without affecting surrounding tissues, the approach could be a safer and more precise alternative to current therapeutic approaches. The results are published in Cell Reports Medicine.
“Cancer stem cells are like shapeshifters,” said Pradipta Ghosh, M.D., senior author of the study and professor of medicine and cellular & molecular medicine at UC San Diego School of Medicine. “They play hide-and-seek inside tumors. Just when you think you’ve spotted them, they disappear or change their identity. It’s like trying to hold on to a wet bar of soap in the shower.”
“What surprised us most was that after we reprogrammed the cancer stem cells to behave like normal cells, they chose to self-destruct instead,” said first author Saptarshi Sinha, Ph.D., interim director of the Center for Precision Computational Systems Network (PreCSN), part of the Institute for Network Medicine (iNetMed) at UC San Diego School of Medicine.
“It was as if they couldn’t live without their cancerous identity.”
To demonstrate the clinical potential of this approach, the researchers were able to leverage UC San Diego’s HUMANOID™ Center, also part of (iNetMed), to successfully test the drug in patient-derived organoids — tiny, lab-grown replicas of human tumors.
These organoids faithfully preserve the structure, behavior and biology of real cancers, allowing researchers to safely and effectively test treatments in human tissues. Organoid experiments can streamline the process of bringing treatments to clinical trials, as many therapies that succeed in animal models ultimately fail in humans.
“It’s like doing clinical trials in a dish, which collapses timelines from years to months,” said Ghosh, who is also director of the HUMANOID™ Center. “We used a complete suite of cell analysis platforms at the Agilent Center of Excellence to measure not just whether a drug works, but how precisely and safely it works, before it ever reaches a patient.”
“It truly takes a village to get it right, and we’re fortunate to have the kind of partnerships that allow us to stay nimble yet impactful,” added Ghosh.
The team is also diving deeper into the question posed by their results: what made the cancer stem cells spontaneously die? Cracking that code could unlock an entirely new arsenal of therapies.
“This isn’t just about colon cancer,” said Ghosh. “CANDiT is an end-to-end human roadmap — we can apply it to any tumor, find the right targets, and finally take aim at the cells that have been the hardest to define, track or treat. By constantly anchoring small-scale organoid insights to Phase 3–sized human diversity in the clinic, we can build discoveries that are rigorous, reproducible and scalable, all without losing sight of the essentials of human disease. The potential of this approach to transform clinical medicine is not just immense — it’s inevitable.”
Reference: Sinha S, Alcantara J, Perry K, et al. CANDiT: A machine learning framework for differentiation therapy in colorectal cancer. CR Med. 2025;0(0). doi: 10.1016/j.xcrm.2025.102421
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