Computer model can identify tumor-fighting immune cells

A new computer model will allow experts to identify specific immune cells that battle tumors in patients with lung cancer.

The research team has demonstrated that their three-gene “MANAscore” computer model can identify the cells targeted by immune checkpoint inhibitor therapies.

The model has also played a significant role in explaining the differences in patient responses to immunotherapy.

“Mutation-associated neoantigens (MANAs) are a target of antitumor T-cell immunity. Sensitive, simple, and standardized assays are needed to assess the repertoire of functional MANA-specific T cells in oncology,” noted the researchers.

Activating tumor-destroying cells

Study senior author Dr. Kellie Smith is an associate professor of oncology at Johns Hopkins University.

“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. If we can study them, that means we can identify better biomarkers and better targets for combination immunotherapy,” said Dr. Smith.

Immunotherapies, such as PD-1 inhibitors, are becoming increasingly useful in treating various types of cancer. They function by activating tumor-destroying immune cells – known as T cells – that are rendered inactive by the protein PD-1.

PD-1 inhibitors re-activate the T cells, thereby fortifying the patient’s immune system to fight cancer more effectively.

However, these therapies don’t work for everyone, and scientists must understand why so they can develop more effective treatments.

Identifying elusive immune cells

Study co-author Dr. Zhen Zeng is a bioinformatics research associate at the John Hopkins Kimmel Cancer Center.

“Tumor-active T cells are very important to a patient’s response to therapy, but they are difficult to find,” explained Dr. Zeng.

Dr. Smith assisted in the development of a technology known as MANAFEST (Mutation-Associated NeoAntigen Functional Expansion of Specific T Cells), which was first introduced in 2018.

The team combined MANAFEST with single-cell sequencing to identify these elusive immune cells in six patients with lung cancer.

This process, which spanned several years and cost millions of dollars, showed that immunotherapy-activated immune cells share a common gene expression profile. The study laid the groundwork for the MANAscore model.

Simplifying the identification process

“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,” explained Dr. Smith.

“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 discerned significant differences in the T cells activated in the tumors of patients who respond to immune checkpoint therapy compared to those who don’t.

Responding patients possess a higher proportion of stem-like memory T cells, which act as a reservoir for new cells and can develop into many effective anti-tumor cells, Dr. Zeng noted.

While this may explain the differences in patent responses, more research is required to validate these findings.

Revolutionizing cancer treatments

Currently, the team is focused on developing a clinical test that employs multispectral immunofluorescence panels to identify the three-gene signature of immune therapy responding T cells.

“We hope to translate our three-gene signature into a biomarker that clinicians can use to guide cancer care,” said Dr. Smith.

Using the new model, experts can also study the spatial data to understand whether interactions between tumor-targeting T cells and other types of immune cells affect clinical outcomes.

This work could potentially revolutionize cancer care, making treatments more personalized and effective.

Expanding the reach of cancer immunotherapy

Beyond lung cancer, researchers believe the MANAscore model could be applied to other cancers treated with immune checkpoint inhibitors.

By refining the model and expanding its use, scientists aim to develop a universal tool for identifying responsive immune cells across different cancer types.

Ultimately, this research could lead to broader applications in precision oncology, making immunotherapy more effective for a wider range of patients.

The full study was published in the journal Nature Communications.

—–

Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. 

Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

—–