Researchers at the Johns Hopkins Kimmel Cancer Center and the Bloomberg-Kimmel Institute for Cancer Immunotherapy have made a groundbreaking advancement in cancer immunotherapy. They have developed a computer model designed to help scientists identify the tumor-fighting immune cells in patients with lung cancer who are treated with immune checkpoint inhibitors. The innovative model, known as the MANAscore, could change how researchers understand immune responses to cancer treatments and guide the development of more effective therapies, particularly for patients who do not initially respond to immunotherapy.
In their study, which is set to be published on February 3 in Nature Communications, the team demonstrated that their three-gene MANAscore model could precisely pinpoint the immune cells targeted by immune checkpoint inhibitors, a class of drugs that reactivates the body’s immune system to fight cancer. The study, led by Zhen Zeng, Ph.D., a bioinformatics research associate at the Kimmel Cancer Center, also identified key differences in immune responses associated with patients’ varied reactions to immunotherapy.
Immune Checkpoint Inhibitors and Their Role in Cancer Treatment
Immune checkpoint inhibitors have revolutionized the treatment of various cancers, including lung cancer. Drugs like PD-1 inhibitors work by targeting and blocking proteins that inhibit the immune system’s ability to attack cancer cells. Specifically, PD-1 is a protein found on the surface of T cells, a type of immune cell responsible for attacking tumors. When PD-1 binds to its partner protein, PD-L1, which is found on cancer cells, it effectively turns off the T cells, allowing the tumor to evade the immune system.
PD-1 inhibitors work by preventing this binding, effectively “turning on” the immune system, allowing T cells to attack cancer cells. While these therapies have shown remarkable success in treating various cancers, such as non-small cell lung cancer, melanoma, and renal cell carcinoma, they do not work for all patients. In fact, a significant number of patients fail to respond to these therapies, making it crucial for researchers to understand why some patients benefit while others do not.
The Challenge: Identifying Tumor-Fighting Immune Cells
One of the primary obstacles in improving immune checkpoint therapies is identifying the tumor-active T cells that are responsible for fighting the cancer. These immune cells are critical to a patient’s response to therapy, but they are notoriously difficult to find in tumor samples. Scientists have long been trying to better understand which T cells are being activated and how they contribute to treatment outcomes.
Zhen Zeng, Ph.D., explains that the challenge lies in the rarity and difficulty of isolating these immune cells. “Tumor-active T cells are very important to a patient’s response to therapy, but they are difficult to find,” Zeng says. As a result, identifying which immune cells are being targeted by immunotherapies and studying them in greater detail has remained a significant hurdle.
The MANAscore Model: Simplifying Immune Cell Identification
To overcome this challenge, Zeng, along with senior author Kellie Smith, Ph.D., an associate professor of oncology at Johns Hopkins, developed the MANAscore model. This new three-gene model can identify immune cells activated by immune checkpoint inhibitors without the need for the expensive, time-consuming processes traditionally used to identify these cells.
Previously, Smith’s team developed MANAFEST (Mutation-Associated NeoAntigen Functional Expansion of Specific T Cells), a technology that used single-cell sequencing to track and identify tumor-targeting T cells in patients with lung cancer. The original process was highly labor-intensive and costly, requiring years of work and millions of dollars to study just a few patients. But with the advent of the MANAscore, the team has found a way to drastically reduce the time and cost involved in identifying the key immune cells that play a role in immunotherapy responses.
What sets MANAscore apart from other models is its simplicity and efficiency. While most existing models require the analysis of over 200 genes, the MANAscore model uses just three genes to identify the critical tumor-fighting T cells. This makes it not only faster and cheaper, but also more accessible for use in clinical settings.
Differences Between Responders and Non-Responders to Immunotherapy
In addition to identifying the immune cells targeted by immunotherapy, the researchers also used the MANAscore model to analyze the differences between patients who respond to PD-1 inhibitors and those who do not. They discovered key differences in the types of T cells activated in the tumors of responding versus non-responding patients.
The study revealed that patients who responded to the therapy exhibited a higher proportion of stem-like memory T cells in their tumors. These T cells are particularly valuable because they act as a reservoir for new immune cells and have the potential to develop into a wide variety of highly effective anti-tumor cells. The stem-like characteristics of these T cells likely enable them to multiply more effectively, thus producing a larger population of tumor-fighting cells.
These findings suggest that patients who respond to immune checkpoint inhibitors may have a greater ability to generate new immune cells that are specifically tailored to attack their tumors. This ability to self-renew and persist over time could help explain why some patients are able to mount a more robust and lasting immune response to treatment.
Zeng further explains, “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.”
This discovery could be important for improving treatment strategies. It suggests that therapies designed to enhance the stem-like properties of T cells could potentially improve the response rates for patients who do not initially respond to immune checkpoint inhibitors.
Towards Clinical Applications: Biomarkers for Immunotherapy
The team’s next goal is to translate the three-gene signature identified in their study into a clinical test that can be used in patient care. They are currently developing a multispectral immunofluorescence panel that would allow clinicians to detect the three-gene signature in tumor samples, providing a simple and effective way to identify which patients are likely to respond to immune checkpoint inhibitors.
By using this three-gene signature as a biomarker, clinicians could potentially identify patients who are most likely to benefit from these therapies, enabling them to tailor treatments more effectively. This could be particularly useful in designing combination immunotherapies, where multiple therapies are used together to enhance the immune response.
Additionally, Zeng is exploring how the proximity of immune cells with the three-gene signature to other immune cells, such as regulatory T cells, might influence the overall immune response. This spatial relationship could help scientists better understand how different immune cells interact with each other within the tumor microenvironment, ultimately affecting treatment outcomes.
Zeng is also collaborating with other research labs across the country to see if the MANAscore can be applied to other types of cancer. By creating a database of single-cell sequencing data from different cancer types, the team hopes to identify cancer-type-specific characteristics of immune cells that respond to immune checkpoint therapy. This could open the door to using the MANAscore model as a universal tool for predicting immunotherapy responses across various cancers.
Conclusion
The development of the MANAscore model marks a significant step forward in the quest to improve cancer immunotherapy. By simplifying the identification of tumor-fighting T cells and uncovering key differences between responders and non-responders, this new model could help researchers develop more personalized and effective treatment strategies for cancer patients.
As the team works to validate their findings and make the model clinically accessible, the hope is that the three-gene signature will become a valuable tool in the fight against cancer, offering clinicians a better way to predict and improve patient outcomes. If successful, this approach could lead to more precise and effective immunotherapies, ultimately improving the lives of countless cancer patients.