Predicting Treatment Responses: Scientists uncover strategies for determining immunotherapy success rates.
Every year, scientists strive to find new ways to combat cancer, and one promising approach is immunotherapy. However, not every individual or cancer type can be treated with this method. Researchers from Johns Hopkins University in Maryland have made a significant stride by identifying a specific subset of tumor mutations that indicate how receptive a tumor will be to immunotherapy.
The team of researchers believes their discovery will help doctors more accurately select patients for immunotherapy and better predict its outcomes. Their findings were recently published in the journal Nature Medicine.
Immunotherapy uses the body's immune system to fight disease. Usually, cancer cells develop mutations that allow them to evade detection by the immune system. Immunotherapy offers a boost to the immune system, making it easier for it to identify and destroy cancer cells. There are various types of immunotherapy, including checkpoint inhibitors, adoptive cell therapy, and cancer vaccines.
Currently, immunotherapy is used to treat breast cancer, melanoma, leukemia, and non-small cell lung cancer. Researchers are examining its potential for treating other cancer types as well, such as prostate, brain, and ovarian cancer.
The doctors' initial approach to determining how well a tumor will respond to immunotherapy is by measuring the total number of mutations in the tumor, also known as tumor mutational burden (TMB). However, this method does not consistently predict which patients will benefit due to biological and technical complexities.
Johns Hopkins researchers focused on a subset of mutations within the overall TMB, which they called "persistent mutations." Persistent mutations are always present in cancer cells, making the cancer tumor continually visible to the immune system. This allows for a better response to immunotherapy.
"Persistent mutations may render the cancer cells continuously visible to the immune system, which, when augmented with immune checkpoint blockade, results in the immune system eliminating cancer cells over time," said Dr. Valsamo Anagnostou, a senior author of the study and an associate professor of oncology at Johns Hopkins.
This discovery may help doctors more accurately select patients for immunotherapy trials or better predict clinical outcomes. In the future, high-throughput, next-generation sequencing techniques may be used to study patients' tumor mutations and categorize patients by their likelihood of response to immunotherapy.
The team's research highlights the importance of identifying and focusing on immunogenic "quality" mutation-associated neoantigens within the broader TMB. These specific mutations are more likely to indicate how receptive a tumor will be to immunotherapy. This "biological calibration" of TMB takes into account mutation contexts and the tumor genomic landscape, enhancing its clinical utility in guiding immunotherapy decisions.
- The scientists' recent discovery at Johns Hopkins University involves identifying a specific subset of tumor mutations, known as "persistent mutations," which may help doctors more accurately select patients for immunotherapy and predict its outcomes.
- According to Dr. Valsamo Anagnostou, a senior author of the study and an associate professor of oncology at Johns Hopkins, persistent mutations, when combined with immune checkpoint blockade, can make cancer cells continuously visible to the immune system, leading to their elimination over time.
- The Johns Hopkins researchers' findings suggest that focusing on immunogenic "quality" mutation-associated neoantigens within the broader tumor mutational burden (TMB) could be more useful in determining how receptive a tumor will be to immunotherapy, as these specific mutations are more likely to indicate a positive response.
