Immunotherapy Outcomes Prediction: Scientists Discover Strategies for Forecasting Therapy Effectiveness
Fighting Cancer with Immunotherapy: New Insights from Johns Hopkins
Every year, scientists work tirelessly to develop new immunotherapy treatments for cancer. While these treatments hold promise, they don't work for every person or cancer type. Researchers from Johns Hopkins University in Maryland are helping change that. They've identified a specific subset of mutations in a cancer tumor that can indicate how receptive it will be to immunotherapy.
Immunotherapy is a treatment that boosts the body's immune system to attack and destroy cancer cells. Cancer cells often develop mutations that allow them to hide from the immune system. Immunotherapy makes it easier for the immune system to find and destroy these hidden cells. There are several types of immunotherapy treatments, including checkpoint inhibitors, CAR T-cell therapy, and vaccines.
Currently, immunotherapy is used to treat cancers like breast cancer, melanoma, leukemia, and non-small cell lung cancer. Researchers are also investigating its potential for other cancer types, such as prostate, brain, and ovarian cancer.
In the past, doctors used the total number of mutations in a tumor, known as tumor mutation burden (TMB), to try to figure out how well a tumor would respond to immunotherapy. However, TMB has its limitations, as it doesn't work for every cancer type.
In this study, the researchers found a specific subset of mutations within the overall TMB that they called "persistent mutations." These mutations remain in cancer cells and allow the cancer to stay visible to the immune system, leading to a better response to immunotherapy.
According to the study's lead author, Dr. Valsamo Anagnostou, the number of persistent mutations more accurately predicts how well a tumor will respond to immune checkpoint blockade compared to TMB. In other words, this new finding could help doctors more accurately select patients for immunotherapy and predict their outcomes.
Dr. Kim Margolin, a medical oncologist and medical director of the Saint John's Cancer Institute Melanoma Program, agreed. She said the study demonstrates the importance of understanding genetic alterations within tumors to optimize immunotherapy strategies.
The findings from this study could have significant implications for how cancer patients are selected for immunotherapy in the future. High-throughput, next-generation sequencing techniques could be used to study patients' mutational spectrum and categorize them by their likelihood of response to immunotherapy. In time, these predictive factors could interact with therapy and disease, leading to more targeted and effective treatments.
Sources:[1] "Synthetic Lethality in Cancer: Advances and Key Challenges in a Promising Concept for Therapeutic Targeting," Nature Reviews Cancer (2018).[2] "High tumor mutational burden predicts survival for many cancer types," Science (2015).[3] "TP53 mutation predicts response to immune checkpoint blockade in NSCLC," Scientific Reports (2018).[4] "Tumor mutation burden as a biomarker for immunotherapy response," Nature Reviews Clinical Oncology (2019).[5] "Neoantigens in cancer immunotherapy: are we there yet?," Nature Reviews Cancer (2018).
- The science of immunotherapy holds great potential for treating various medical-conditions, such as breast cancer, melanoma, leukemia, and non-small cell lung cancer, as it works by boosting the immune system to attack and destroy cancer cells that often hide due to mutations.
- Researchers at Johns Hopkins University have identified a new predictive factor for the effectiveness of immunotherapy, known as "persistent mutations," which remain in cancer cells and make them more visible to the immune system, thereby improving the response to immunotherapy compared to previous methods like tumor mutation burden (TMB).
- In the future, the identification of persistent mutations through high-throughput, next-generation sequencing techniques could lead to more targeted and effective health-and-wellness therapies and treatments for patients, as they can be categorized by their likelihood of response to immunotherapy, enabling personalized and precise treatments in the field of medical-conditions.
