Predicting Immunotherapy Success: Strategies Developed by Scientists
Every year, researchers make advancements in the fight against cancer. One of the newest options is immunotherapy, which uses the body's immune system to combat the disease.
However, not everyone and every type of cancer can benefit from immunotherapy. Researchers are still trying to find out why some tumors are resistant to this treatment.
Recently, researchers from Johns Hopkins University in Maryland have made a breakthrough. They discovered a specific subset of mutations in cancer tumors that indicate how receptive it will be to immunotherapy.
These researchers believe their findings will help doctors more accurately select patients for immunotherapy and predict outcomes from the treatment.
Their work was recently published in the journal Nature Medicine.
What is immunotherapy?
Immunotherapy is a treatment that boosts the body's immune system to fight cancer cells. Usually, cancer cells develop mutations that allow them to hide from the immune system. Immunotherapy helps the body find and destroy these cancer cells.
There are various types of immunotherapy, including checkpoint inhibitors, cancer vaccines, and CAR T-cell therapy.
The Role of Persistent Mutations
Currently, doctors use the total number of mutations in a tumor, called Tumor Mutation Burden (TMB), to estimate how well a tumor will respond to immunotherapy.
However, Johns Hopkins University researchers have identified a specific subset of mutations within the overall TMB, which they call "persistent mutations." These mutations are less likely to disappear as cancer evolves, allowing the cancer tumor to remain visible to the immune system.
As a result, there's a better response to immunotherapy. The researchers believe that the number of persistent mutations more accurately predicts how likely a tumor is to respond to immunotherapy compared to the overall Tumor Mutation Burden.
Impact on Patient Care
Dr. Valsamo Anagnostou, a senior author of the study and associate professor of oncology at Johns Hopkins, believes that this new knowledge will help doctors more accurately select patients for immunotherapy and predict treatment outcomes.
Dr. Kim Margolin, a medical oncologist, also agrees that these findings will likely change how cancer patients are selected for immunotherapy in the future. She believes that high-throughput sequencing techniques will be used to study patients' mutational spectrum and categorize patients by their likelihood of response to immunotherapy.
Ultimately, these insights could lead to more effective immunotherapy treatments for cancer patients.
Addition Insights
- Persistent mutations are mutations that don't disappear as cancer evolves, making cancer cells more visible to the immune system.
- These persistent mutations often create neoantigens, which are highly immunogenic and can improve the response to immunotherapy.
- Tumors with a high number of persistent mutations are more likely to respond to immune checkpoint blockade compared to tumors with a high overall mutation load.
- Advance computational frameworks like ProgModule have identified specific driver modules associated with higher immunotherapy response rates.
- Immunotherapy has shown promise in various types of cancer, including breast cancer, melanoma, leukemia, and non-small cell lung cancer. Researchers are currently investigating its potential for other types of cancer, such as prostate cancer, brain cancer, and ovarian cancer.
Immunotherapy is a treatment that boosts the body's immune system to fight cancer cells. Importantly, Johns Hopkins University researchers have discovered a specific subset of mutations they call "persistent mutations," which are less likely to disappear as cancer evolves, making cancer cells more visible to the immune system. These persistent mutations often create neoantigens, which can improve the response to immunotherapy and make tumors with a high number of persistent mutations more likely to respond to immune checkpoint blockade compared to tumors with a high overall mutation load.
