Study Shows Computer Model Can Help Identify Tumor-Fighting Immune Cells in Patients with Lung Cancer

A study published in Nature Communications shows that researchers have “developed a computer model to help scientists identify tumor-fighting immune cells in patients with lung cancer treated with immune checkpoint inhibitors.”

The research team “demonstrated that their three-gene ‘MANAscore’ computer model can identify the immune-cells targeted by immune checkpoint inhibitor therapies. It also helped the team identify differences associated with patient response to immunotherapy.”

Immune checkpoint inhibitors like PD-1 inhibitors “are available to treat dozens of cancer types. These revolutionary therapies work by unleashing tumor-killing immune cells, called T cells, that are switched off by the protein PD-1. PD-1 inhibitors turn the patient’s T cells back on, allowing patients’ immune systems to fight cancer more effectively. But not all patients respond to these therapies, and scientists need to know why in order to develop improved therapies that help nonresponders.”

The researchers’ approach combined the MANAFEST technology with “single-cell sequencing to identify these rare immune cells in six patients with lung cancer, a laborious process that took several years and cost millions of dollars.” Kellie Smith, the study’s senior author, says that this method allows researchers to “skip a time-consuming and expensive process to identify the cells targeted by immunotherapy, and will help [them] identify what distinguishes who will respond to these therapies.”

The team also found “key differences in the T cells activated in the tumors of patients who respond to immune checkpoint therapy compared with those who don’t,” which may explain why some “patients are more able to respond.”