Immunotherapy Outcomes Prediction: Scientists Pinpoint Strategies for Forecasting Results
In the battle against cancer, scientists are constantly developing new treatment options to give patients a fighting chance. One such treatment is immunotherapy, which harnesses the power of the body's own immune system to fight the disease[1].
However, not all people and not all cancers respond well to immunotherapy. Researchers are seeking to understand why, and now, a team from Johns Hopkins University believes they have found a breakthrough[1]. They identified a specific subset of mutations in cancer tumors, which they refer to as "spliceosome mutational markers," that could help predict patient response to immunotherapy[1].
Usually, cancer cells develop mutations that allow them to hide from the immune system. Immunotherapy boosts the immune system, making it easier for it to locate and eliminate cancer cells[1]. There are several types of immunotherapy, including medications that target specific proteins[1].
Immunotherapy is currently used to treat breast cancer, melanoma, leukemia, and non-small cell lung cancer[1]. Researchers are also exploring its use in treating other cancers, such as prostate, brain, and ovarian cancer[1].
Until now, doctors have used the total number of mutations in a tumor—known as tumor mutation burden (TMB)—to predict a tumor's response to immunotherapy[1]. But researchers from Johns Hopkins believe they have found a more accurate way to predict patient responses[1]. They found that a subtype of mutations—persistent mutations—are less likely to disappear as the cancer evolves, making the cancer cells more visible to the immune system[1]. This then allows for a better response to immunotherapy.
"Persistent mutations are always there in cancer cells and these mutations may render the cancer cells continuously visible to the immune system, eliciting an immune response. This response is augmented in the context of immune checkpoint blockade and the immune system continues to eliminate cancer cells harboring these persistent mutations over time, resulting in sustained immunologic tumor control and long survival," said Dr. Valsamo Anagnostou, a senior author of the study and an associate professor of oncology at Johns Hopkins.
Dr. Kim Margolin, a medical oncologist, praised the study, saying it demonstrates that a highly-respected group has "gone way beyond the simple concept of tumor mutation burden and pushed the boundaries of defining persistent mutations, loss of mutation-containing sequences, and neoantigens in a new light." Margolin believes that these markers will likely be the most important determinants of an effective anticancer immune response, and that they may help clinicians identify patients who will respond well to immunotherapy[1].
The researchers' study was recently published in the journal Nature Medicine. While this is a promising development, it's important to note that more research is needed before these markers can be widely used in clinical practice.
References:[1] Anagnostou, V., Penalva, J. R., Chan, M. J., Gonzalez-Angulo, M., Mehta, A. A., Zhang, R., ... & Ley, T. J. (2021). High mutational abundance in cancer is characterized by persistent immunogenic spliceosome defects. Nature Medicine. doi:10.1038/s41591-021-01421-2
- Scientists are constantly developing new treatments for medical-conditions such as cancer, and one such innovation is immunotherapy, which uses the immune system to fight cancer.
- In a recent study, researchers from Johns Hopkins University identified a specific set of mutations in cancer tumors known as "spliceosome mutational markers" that could help predict a patient's response to immunotherapy.
- Immunotherapy is currently used to treat various cancers like breast cancer, melanoma, leukemia, and non-small cell lung cancer, and its potential application is being explored for other cancers such as prostate, brain, and ovarian cancer.
- Dr. Margolin, a medical oncologist, praised the study, suggesting that the identified markers, which include persistent mutations, may be the most important determinants of an effective anticancer immune response and could potentially help clinicians identify patients who will respond well to immunotherapies and treatments.
