Use clinical and genomic data to predict the effects of immunotherapy

Immune checkpoints have always been a hot spot for tumor immunotherapy.

Researchers from the famous Cleveland Clinic have developed a machine learning model that integrates learning from the genomes of a cohort (MSK-IMPACT) of 1,479 patients with 16 different cancer types who have undergone immune checkpoint blocking ICB therapy , Molecular, demographic, and clinical data can be used to predict patient response to immune checkpoint blockade therapy

The new prediction tool evaluates a variety of patient-specific biological and clinical factors to predict the degree of response to immune checkpoint inhibitors and survival outcomes

"Knowing which treatment is best for the patient is important," said Dr.

Immune checkpoints are proteins on specific immune cells (T cells).

Interestingly, the researchers found that the variable that has the greatest impact on ICB response is tumor mutational burden (the frequency of certain mutations in tumor genes), followed by the patient's chemotherapy history

Chan said, "How these variables work together is the key