Predicting Immunotherapy Response: Scientists Discover Methods for Anticipating Result Variations
Each year, scientific advancements bring forth new cancer treatments, and one of the latest is immunotherapy. But note, immunotherapy doesn't work for every person or cancer type. Researchers at Johns Hopkins University have made a breakthrough by discovering a specific mutation subset within cancer tumors. These specific mutations, dubbed "persistent mutations," could help doctors more accurately choose individuals for immunotherapy and forecast treatment outcomes.
Their findings were published in the journal Nature Medicine.
Immunotherapy leverages the body's immune system to combat diseases. Normally, cancer cells possess mutations that hide them from the immune system. Immunotherapy boosts the immune system to identify and destroy cancer cells more effectively.
Various types of immunotherapy exist, including checkpoint inhibitors, CAR T-cell therapy, and adoptive cell transfer. These treatments are currently utilized for breast cancer, melanoma, leukemia, and non-small cell lung cancer, while clinical trials explore their application for other cancers like prostate, brain, and ovarian cancer.
Researchers currently use the total number of mutations in a tumor, referred to as tumor mutation burden (TMB), to try to determine how well a tumor will respond to immunotherapy. However, this study identified persistent mutations within the overall TMB, which remain even as cancer evolves. This helps cancer tumors remain visible to the immune system, allowing for a better response to immunotherapy.
Persistent mutations can render cancer cells continuously visible to the immune system, triggering an immune response. This response is intensified by immune checkpoint blockade, ensuring cancer cells harboring these persistent mutations continue to be eliminated over time, resulting in sustained immunologic tumor control and extended survival. The number of persistent mutations more effectively predicts tumors that are likely to respond to immune checkpoint blockade compared to the overall tumor mutation burden.
These findings may revolutionize how cancer patients are chosen for immunotherapy in the future. High-throughput, next-generation sequencing techniques may become crucial for studying patients' mutational spectrum. This would enable us to categorize patients according to their likelihood of response to immunotherapy or benefit from other treatments, ultimately leading to personalized and more effective treatment strategies.
[1] Synthetic lethal co-mutations in DNA Damage Response pathways as biomarkers for immunotherapy response[2] Synthetic lethality as a guide for precision cancer immunotherapy[3] Tumor mutational burden as a biomarker for immunotherapy response[4] Predicting response to immune checkpoint blockade using tumor mutational burden and neoantigen load
- The study published in Nature Medicine reveals that a specific subset of mutations, known as "persistent mutations," could aid doctors in selecting suitable individuals for immunotherapy and predicting treatment outcomes.
- Immunotherapy treatment, which boosts the immune system to combat cancer, has gained prominence in health-and-wellness sciences, although it may not be effective for every person or cancer type.
- Researchers at Johns Hopkins University have identified that persistent mutations within the overall Tumor Mutation Burden (TMB) may render cancer cells continuously visible to the immune system, thereby improving the response to immunotherapy.
- The number of persistent mutations provides a more effective prediction of tumors likely to respond to immune checkpoint blockade therapy compared to the overall Tumor Mutation Burden, potentially revolutionizing the selection of cancer patients for immunotherapy.
