AI model predicts effective immunotherapy combinations for liver cancer
· News-MedicalThe tool needs to be validated further before it could be incorporated into clinical treatment, he says.
In this study, Deshpande and colleagues expanded the platform to model fibroblasts - a cell type previously associated with resistance to immunotherapy in liver cancer - and developed a machine-learning calibration workflow that tunes the simulation to data from real clinical trials, generating virtual patients whose predicted responses can be checked against actual outcomes.
One advantage of a computational model is scale, he says. From a small, early-phase (phase I) study of 15 patients, it can generate a phase III–sized virtual population, letting researchers estimate how a therapy might perform in a far larger trial - quickly and without risk to anyone. When the team simulated treatment with the targeted therapy cabozantinib and the immunotherapy nivolumab, alone and together, the predicted response rates tracked closely with those reported in real clinical trials - evidence that the virtual patients behave like real ones. The team also validated the model's predicted tumor architectures against real post-treatment tissue and compared the microenvironments of responders and non-responders.
Ideally, modeling could get to the point where if multiple treatments were available for a particular cancer, researchers could help determine which of the treatments would be most effective, or which to avoid, Deshpande says. Because such architectural features - both the fibroblast barrier the model flags and structural patterns the team measured in patient tumors - are visible before treatment begins, they could eventually help predict who will benefit, the researchers say.
The study was co-supervised by Popel and Elana Fertig, Ph.D., director of the Institute of Genome Science at the University of Maryland School of Medicine; co-authors were Shuming Zhang, Hanwen Wang, Yeonju Cho, Wendy Wong, Mark Yarchoan, Elizabeth Jaffee, Won Jin Ho and Luciane Kagohara of Johns Hopkins; and Heber Rocha of Indiana University.
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