Efficacy of Cancer Immunotherapy Clarified by Computer Model

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Efficacy of Cancer Immunotherapy Clarified by Computer Model


Efficacy of Cancer Immunotherapy Clarified by Computer Model
Credit: Marcin Klapczynski/Gatty Images

Researchers at the Johns Hopkins University School of Medicine have developed a simplified, computer-based model to help detect tumor active T cells, which are activated by immune checkpoint inhibitor immunotherapy.

The model uses information from three genes (CXCL13, ENTPD1 and IL7R) to look for active T cells. The team is now working to develop a test that can identify cells responding to therapy in cancer patients undergoing immunotherapy treatment.

“We have developed a way to identify the cells directly targeted by immune checkpoint inhibitors, and if we can identify them, we can study them,” said the study’s lead author, Kellie Smith, an associate professor of oncology at Johns Hopkins, in a press statement. “If we can study them, that means we can identify better biomarkers and better targets for combination immunotherapy.”

As of January 2025, the FDA has approved 12 immune checkpoint inhibitors to treat a variety of different cancers, nine of which are either PD-1 inhibitors (eg. pembrolizumab or nivolumab) or PD-L1 inhibitors (eg. atezolizumab).

Checkpoint proteins on T immune cells usually stop the body’s immune system from attacking itself, but cancers have found ways to use these proteins to avoid being attacked by the immune system. Checkpoint inhibitors block the action of these proteins and allow T cells to identify and destroy cancer cells more effectively.

However, while these drugs are very effective in some patients, response rates can be as low as 20 percent depending on the drug, cancer and population. Research is ongoing to find out why some patients respond, and some do not, but having an accurate test that could look for biomarkers indicating a positive or negative response to therapy would be useful.

Smith and colleagues previously carried out work involving single cell sequencing from patients with lung cancer and were able to find a specific gene expression profile shared by the activated T cells. In the current study, published in Nature Communications, they developed this work further to develop a computer-based model called MANAscore.

“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 said.

“We’re not the first to come up with one of these models, but what sets ours apart is that it uses only three genes, while the most commonly used model requires more than 200 genes. Ours is simpler and easier to use.”

The team also noticed that people who respond to these therapies have a higher proportion of stem-like memory T cells, which can more effectively build new cells such as effective anti-tumor cells.

“The stem-like characteristics of T cells are critical because they enable self-renewal and long-term persistence. This allows for sustained immune responses and the ability to expand into a robust population of effector T cells when needed,” explained first author Zhen Zeng, PhD, a bioinformatics research associate at the Kimmel Cancer Center.

The team now wants to develop a test that clinicians can use to help guide cancer care by identifying if patients have tumor responsive T cells after treatment.



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