Machine learning reveals rules of T cell activation

To understand complex communications between tumors and immune cells, some cancer researchers are turning to machine learning, a form of artificial intelligence that can learn from and find patterns in data without being explicitly programmed. A new model developed by CCR and McGill University scientists uses machine learning to predict how tumor antigens — immune-stimulating molecules on the surface of tumor cells — impact the behavior of T cells. The model is based on extensive experimental data tracking signals that T cells generate after they encounter particular antigens. Those signals ultimately determine how the immune cells respond to a potential threat.

The model, reported May 20, 2022, in Science, was developed by the group of Grégoire Altan-Bonnet, Ph.D., Senior Investigator in the Laboratory of Integrative Cancer Immunology, in collaboration with Paul François’ group at McGill University in Montréal. It has helped clarify the factors that shape T cell responses to tumors, which Altan-Bonnet says could accelerate the development of cancer immunotherapies.

Cancer immunotherapies can help patients’ T cells find and destroy cancer cells, but even with these treatments, some patients’ immune systems fail to mount an effective anticancer response. Researchers have struggled to develop immunotherapies that work for a majority of patients, partially due to the complexity of the signaling triggered when a T cell encounters a cancer cell, Altan-Bonnet says.

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