AI tools measure ‘immunological fitness’ to personalize cancer therapies

Researchers from the Data Science and Artificial Intelligence Institute (DATAI) at the University of Navarra have developed artificial intelligence (AI) models to personalize immune therapies for oncology patients.

The study is published in the journal Journal for ImmunoTherapy of Cancer.

The analysis used data from more than 3,000 patients with lung and urothelial cancer (the third and sixth most frequently diagnosed cancers in the United States in 2024, according to the National Cancer Institute). By employing machine learning models, the researchers identified novel genetic signatures specific to each stage of the disease, and created the “IFIT score,” a system that will help personalize the immunotherapy treatment, improving its effectiveness.

The “IFIT score” is a measure or an index of the “immunological fitness” of cancer patients at each stage of a patient’s disease. It allows patients to be classified according to their risk at each stage of the disease.

“This can help predict response to therapy based on the activity of the patient’s immune system at different stages of cancer treatment,” explains Rubén Armañanzas, leader of DATAI’s Digital Medicine Laboratory and one of the study’s lead authors.

According to the expert, “Immunotherapy represents one of the most promising frontiers in the fight against cancer, and by using AI models, we can further fine-tune treatments based on each patient’s immune profile.”

The University of Navarra study was presented in Houston (United States), during the Society for Immunotherapy of Cancer (SITC 2024) conference. This meeting brings together international leaders from academia, regulatory and governmental bodies, as well as representatives from the pharmaceutical industry, to offer the latest advances in cancer immunotherapy.

IFIT score: A system to personalize cancer treatments

The research, nominated as one of the top 100 presentations at the conference, focuses on analyzing the cancer immunity cycle (CIC). This cycle helps to understand how signals from the immune system affect the effectiveness of immunotherapy treatments.

Using artificial intelligence tools, the researchers have identified specific cellular activity patterns based on the disease’s molecular stage. They have also developed the IFIT “physical immunity” index. This breakthrough highlights the importance of artificial intelligence in personalized medicine, providing new hope in the fight against cancer.

The researchers emphasize that this technique will be further refined through future collaborative studies involving other types of cancer.

This research results from a research camp organized by Institut Roche for centers in the imCORE Network. This international network brings together leading centers of excellence in immuno-oncology worldwide. This global collaboration, involving the Cancer Center Clínica Universidad de Navarra and other cancer research institutions from 10 countries worldwide, underscores the collective effort in searching for innovative cancer approaches.