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Predicting Treatment Success in Immunotherapy: Scientists Disclose Strategies for Anticipating Results

Immunotherapy Outcome Predictions: Scientists Uncover Strategies for Forecasting Effectiveness

Scientists explore means to enhance immunotherapy's cancer-combatting efficiency, as SAUL LOEB/AFP...
Scientists explore means to enhance immunotherapy's cancer-combatting efficiency, as SAUL LOEB/AFP via Getty Images captures the scene.

Predicting Treatment Success in Immunotherapy: Scientists Disclose Strategies for Anticipating Results

Each year, the fight against cancer sees advancements, with immunotherapy being one of the latest treatment options. However, this approach does not work for everyone or every type of cancer. Researchers from Johns Hopkins University in Maryland have recently identified a specific subset of tumor mutations that could help doctors determine a patient's likelihood of responding to immunotherapy.

The research team, led by Dr. Valsamo Anagnostou, believes these findings could improve the selection process for immunotherapy and better predict treatment outcomes. Their work was published in the journal Nature Medicine.

Immunotherapy leverages the body's immune system to combat disease. Typically, cancer cells develop mutations that allow them to evade detection by the immune system. Immunotherapy enhances the immune system's ability to find and destroy cancer cells.

Currently, immunotherapy is a treatment option for certain cancers, such as breast cancer, melanoma, leukemia, and non-small cell lung cancer. Research is ongoing to explore the use of immunotherapy in other types of cancer, including prostate, brain, and ovarian cancer.

For immunotherapy to work effectively, the number of mutations in a tumor—known as tumor mutation burden (TMB)—plays a crucial role. High TMB levels suggest better responses to treatments like anti-CTLA-4 or anti-PD-1/PD-L1 therapies.

In this study, the researchers focused on a subset of mutations within the overall TMB, which they termed "persistent mutations." These mutations remain in cancer cells as the disease evolves, keeping the tumor visible to the immune system and improving the response to immunotherapy.

The number of persistent mutations is a more accurate indicator of a tumor's receptiveness to immune checkpoint blockade compared to overall TMB, according to Anagnostou. This molecular signature could assist in more accurately selecting patients for immunotherapy clinical trials or predicting clinical outcomes.

In an interview with Medical News Today, Dr. Kim Margolin, a medical oncologist, praised the study for moving beyond the simple concept of TMB. Margolin noted that persistent mutations and their associated neoantigens, when recognized by the immune system, play a crucial role in stimulating an effective anticancer immune response.

As high-throughput, next-generation sequencing techniques become more common, it may soon be possible to categorize patients by their likelihood of responding to immunotherapy or benefiting from it after surgery. According to Margolin, this classification could potentially evolve from simple prognostic indicators to predictive factors that interact with therapy, disease, and even the immune tumor environment.

  1. The research team at Johns Hopkins University identified a specific subset of tumor mutations, known as persistent mutations, which could help doctors better predict treatment outcomes for immunotherapy.
  2. The number of persistent mutations, according to Dr. Valsamo Anagnostou, is a more accurate indicator of a tumor's receptiveness to immune checkpoint blockade compared to overall TMB.
  3. This molecular signature of persistent mutations could assist in more accurately selecting patients for immunotherapy clinical trials or predicting clinical outcomes.
  4. As high-throughput, next-generation sequencing techniques become more common, it may soon be possible to categorize patients by their likelihood of responding to immunotherapy or benefiting from it after surgery.

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