Immunotherapy Outcomes Prediction: Researchers Discover Methods to Enhance Forecasting
Every year, the fight against cancer intensifies as researchers develop novel treatment options. One such option gaining traction is immunotherapy, a treatment that enlists the body's immune system to combat the disease. However, not all patients and cancers respond favorably to immunotherapy. To address this, a team of researchers from Johns Hopkins University in Maryland has identified a specific subset of mutations within cancer tumors that indicate a better response to immunotherapy.
The researchers believe their findings will assist doctors in more accurately selecting patients for immunotherapy and predicting treatment outcomes more effectively. Details of their study have been published in the journal Nature Medicine.
Immunotherapy capitalizes on the body's immune system to identify and destroy cancer cells. Cancer cells often develop mutations that allow them to evade the immune system. Immunotherapy provides a boost to the immune system, making it easier for it to locate and eliminate cancer cells. There are several types of immunotherapy, including checkpoint inhibitors and CAR T-cell therapy.
The study's researchers found a subset of mutationsPersistent mutations within the overall Tumor Mutation Burden (TMB) that remain in cancer cells as the disease evolves. This makes the cancer cells more visible to the immune system, enhancing the response to immunotherapy.
"Persistent mutations are perpetually present in cancer cells, and these mutations may render the cancer cells continuously visible to the immune system, eliciting an immune response," said Dr. Valsamo Anagnostou, a senior author of the study and associate professor of oncology at Johns Hopkins.
The team found that the number of persistent mutations more accurately predicts a tumor's response to immune checkpoint blockade than the overall TMB. "Persistent mutation load may help clinicians more accurately select patients for clinical trials of novel immunotherapies or predict a patient's clinical outcome with standard-of-care immune checkpoint blockade," Anagnostou explained.
The discovery could lead to more personalized immunotherapy treatments for cancer patients. For instance, patients with BAP1 mutations have shown remarkable responses to immunotherapy, suggesting that these mutations might serve as predictive biomarkers for the efficacy of immunotherapeutic treatments. Similarly, cancers with microsatellite instability-high (MSI-H) and mismatch repair-deficient (dMMR) status often accumulate frameshift mutations that can produce immunogenic neoantigens, attractive targets for cancer immunotherapy.
The findings offer hope for cancer patients, as they pave the way for more effective and personalized immunotherapy treatments.
- The researchers from Johns Hopkins University identified a specific subset of persistent mutations within cancer tumors that indicate a better response to immunotherapy.
- Dr. Valsamo Anagnostou explains that the number of persistent mutations more accurately predicts a tumor's response to immune checkpoint blockade than the overall Tumor Mutation Burden.
- The discovery could lead to more personalized immunotherapy treatments for cancer patients, as persistent mutations might serve as predictive biomarkers for the efficacy of immunotherapeutic treatments.
