To create the model, the researchers used data from melanoma and lung cancer patients who were being treated with immune checkpoint inhibitors. The model captures aspects of a patient’s tumor evolution and the underlying interactions the tumor had with their immune system to predict how they will respond to immunotherapy.
The researchers said the model has the potential to help physicians identify therapeutic targets within a patient’s immune system and to help scientists develop vaccines for patients who don’t respond to immunotherapy.
“This approach will hopefully lead to better mechanistic predictive modeling of response and future design of therapies that further take advantage of how the immune system recognizes tumors,” said Benjamin Greenbaum, PhD, the senior author of the study, which was published in Nature. Dr. Greenbaum is affiliated with the departments of medicine, hematology and medical oncology, pathology and oncological sciences at The Tisch Cancer Institute at the Icahn School of Medicine.
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