Revolutionary computer Model Simplifies Identification of Cancer-Fighting Immune Cells
Researchers at the Johns Hopkins Kimmel cancer Center and Bloomberg~Kimmel Institute for Cancer immunotherapy have unveiled a groundbreaking computer model designed to identify tumor-fighting immune cells in lung cancer patients treated with immune checkpoint inhibitors. Published on February 3 in Nature Communications, the study introduces the three-gene “MANAscore” model, which simplifies the process of pinpointing immune cells targeted by these therapies and sheds light on patient response differences.
“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,” says Kellie Smith, Ph.D., the study’s senior author and associate professor of oncology at Johns hopkins.
Immune checkpoint inhibitors, such as PD-1 inhibitors, have revolutionized cancer treatment by reactivating tumor-killing T cells that are typically suppressed by the PD-1 protein. While these therapies have shown success across dozens of cancer types, not all patients respond equally. understanding why some patients fail to respond is critical for developing more effective treatments.
“Tumor-active T cells are very vital to a patient’s response to therapy, but they are difficult to find,” explains Zhen Zeng, Ph.D., the study’s first author and a bioinformatics research associate at the Kimmel Cancer Center.
The MANAscore model builds on the team’s earlier work with MANAFEST (Mutation-Associated NeoAntigen Functional Expansion of Specific T Cells), a technology that combines single-cell sequencing to identify rare immune cells. While the original process was time-consuming and costly, MANAscore streamlines this effort by focusing on just three genes, compared to the 200+ genes required by other models.
“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 notes.
The study also revealed key differences in T cells between responders and non-responders. Responders exhibited a higher proportion of stem-like memory T cells, which act as a reservoir for new cells and can develop into effective anti-tumor cells. “The stem-like characteristics of T cells are critical because they enable self-renewal and long-term persistence,” Zeng adds.
the team is now working to translate their findings into a clinical test using multispectral immunofluorescence panels to identify the three-gene signature of therapy-responding T cells. “We hope to translate our three-gene signature into a biomarker that clinicians can use to guide cancer care,” Smith says.
Zeng is also exploring how the proximity of these T cells to other immune cells, such as regulatory T cells, influences immune response. Additionally, the team is collaborating with labs nationwide to determine if MANAscore can be applied to other cancer types, leveraging a database of single-cell sequencing data to identify cancer-specific responder T cell characteristics.
| Key highlights of MANAscore |
|———————————-|
| Purpose | identifies tumor-fighting immune cells in lung cancer patients treated with immune checkpoint inhibitors. |
| Key Feature | Uses onyl three genes, simplifying the process compared to models requiring 200+ genes. |
| Findings | responders show higher proportions of stem-like memory T cells, which may explain better therapeutic outcomes. |
| Next Steps | Developing a clinical test to translate the three-gene signature into a biomarker for cancer care.|
This innovative model marks a significant step forward in personalized cancer treatment,offering hope for improved therapies and better outcomes for patients.
