Johns Hopkins Team Develops Model to Identify T Cells Targeted by Immunotherapy

Scientists at the Johns Hopkins Kimmel Cancer Center have developed a computer model to help scientists identify tumor-fighting immune cells in patients with lung cancer treated with immune checkpoint inhibitors. The model, called MANAscore, uses a three-gene signature to identify tumor-fighting T cells activated by immunotherapy.

Published in Nature Communications, the study led by Kellie Smith and Zhen Zeng, builds on previous research using the MANAFEST technology. While the original method was time-consuming and expensive, MANAscore offers a simpler, more efficient approach to identifying these crucial immune cells.

"Our model allows us to skip a time-consuming and expensive process to identify the cells targeted by immunotherapy, and will help us identify what distinguishes who will respond to these therapies," Smith explains.

The model's simplicity—using only three genes compared to over 200 in other models—makes it particularly user-friendly.

The research also revealed key differences between responders and non-responders to immunotherapy. Patients who respond well to treatment show a higher proportion of stem-like memory T cells, which may explain their ability to generate more tumor-fighting cells.

Looking ahead, the team is working to develop a clinical test based on their three-gene signature. "We hope to translate our three-gene signature into a biomarker that clinicians can use to guide cancer care," says Smith.

Zeng is exploring further applications of the model, including studying cell-to-cell interactions and its potential use in other cancer types. “We want to apply our model to spatial data to learn whether cell-to-cell interactions among tumor-targeting T cells and other cell types affect clinical outcomes,” Zeng says. 

The researchers have created a database of single-cell sequencing data across various cancers to identify cancer-type-specific responder T cell characteristics.