Immunotherapy Outcomes Prediction: Scientists Determine Methods to Forecast Results
In the never-ending quest to eradicate cancer, scientists are constantly developing new treatment options. One exciting new approach is immunotherapy, which leverages the body's immune system to battle this insidious disease. However, it's not a one-size-fits-all solution, and researchers are eager to understand why immunotherapy doesn't work for everyone and every type of cancer.
Maryland-based Johns Hopkins University researchers recently made a breakthrough in this area by identifying a specific subset of mutations in cancer tumors that provides valuable insights into a tumor's receptivity to immunotherapy. This groundbreaking discovery could potentially revolutionize cancer treatment, improving the accuracy of patient selection for immunotherapy and predicting treatment outcomes more accurately.
The Power of Persistent Mutations
Conventional wisdom dictates that doctors gauge a cancer tumor's responsiveness to immunotherapy based on its total number of mutations (known as the tumor mutation burden, or TMB). However, first author and Johns Hopkins researcher, Dr. Valsamo Anagnostou, believes there's a more precise approach: focusing on specific "persistent mutations."
Persistent mutations are a special set of mutations that tend to persist within cancer cells as the cancer evolves. This persistence renders the cancer cells continuously visible to the immune system, enhancing the effectiveness of immunotherapy. Anagnostou explains that these persistent mutations elicit an immune response vital to the elimination of cancer cells, particularly when combined with immune checkpoint blockade.
The researchers found that the number of persistent mutations provides a more accurate indicator of a tumor's receptivity to immunotherapy compared to the overall TMB. Anagnostou believes that using persistent mutation load data could help doctors better select patients for clinical trials of innovative immunotherapies or predict a patient's clinical outcome with standard-of-care immune checkpoint blockade.
The Engaging Dialogue
Medical News Today spoke with Dr. Kim Margolin, a medical oncologist and medical director of the Saint John's Cancer Institute Melanoma Program at Providence Saint John's Health Center in California. Margolin praised the study, bringing attention to the collaborative group's comprehensive examination of persistent mutations, mutation-associated neo-antigens, and the immune system's response.
Margolin emphasized that persistent mutations and mutation-associated neo-antigens, presented by class I and possibly class II major histocompatibility complex (MHC) molecules, are the most critical factors in an effective anticancer immune response. She suggested that immunotherapeutic agents like immune checkpoint-blocking antibodies, cancer vaccines, and even radiation therapy could be optimized through this newfound understanding of persistent mutations and mutation-associated neo-antigens.
The Future of Cancer Treatment
As the research continues, Margolin envisions a not-too-distant future where high-throughput, next-generation sequencing techniques are used to study patients' mutational spectrum, enabling doctors to categorize patients by their likelihood of response to immunotherapy or benefit from monitoring programs for disease-free patients.
Ultimately, persisting mutation load data may evolve from simple prognostic indicators to predictive factors interacting with therapy and disease, paving the way for personalized cancer treatment that considers the unique mutational landscape of each patient's cancer.
Important Examples of Persistent Mutations
- MMR Gene Mutations (MLH1, MSH2): These mutations lead to microsatellite instability, significantly increasing the generation of immunogenic neoantigens, making tumors more susceptible to immune checkpoint blockade therapies like PD-1 inhibitors.
- ATM Mutations: While these mutations might impair antigen presentation by reducing MHC class I molecule production, they also lead to increased expression of immune checkpoint molecules like PD-L1, potentially making tumors more targetable with checkpoint inhibitors.
- POLQ Mutations: Tumors deficient in POLQ may exhibit a greater mutational burden, potentially improving neoantigen presentation and immune stimulation, specifically in breast cancer where POLQ mutations are linked to increased T-cell infiltration.
- DNA Damage Response Mutations: These mutations can lead to increased genomic instability, potentially resulting in a higher neoantigen load, making tumors more responsive to immunotherapy by enhancing immune recognition and activation.
- Immunotherapy, leveraging the immune system, is a promising approach in the fight against cancer, yet its effectiveness varies based on the type of cancer and individual immune systems.
- A recent study by Johns Hopkins University researchers identified specific persistent mutations in cancer tumors, improving the accuracy of patient selection for immunotherapy and predicting treatment outcomes more accurately.
- These persistent mutations, such as MMR gene mutations (MLH1, MSH2), ATM mutations, POLQ mutations, and DNA damage response mutations, can increase the generation of immunogenic neoantigens, enhance neoantigen presentation and immune stimulation, and make tumors more responsive to immunotherapy.
- In the future, high-throughput, next-generation sequencing techniques may allow doctors to categorize cancer patients based on their persistent mutation loads, enabling personalized cancer treatment and improved patient outcomes through the use of therapies like immune checkpoint blockade therapies.
