A newly identified panel of autoantibodies if detected in a patient’s blood prior to immunotherapy, has the potential to reliably predict whether a patient’s cancer would recur and whether they would experience autoimmune side effects as a result of the treatment itself, says a new study.
A new study found that a single research test might be able to tell which patients treated with immunotherapies, which use the immune system to attack cancer cells, are likely to have their cancer come back or have serious side effects.
The research, which was published in Clinical Cancer Research today, focused on a group of immune system signaling proteins known as antibodies that detect foreign bacteria, viruses, and fungi. These blood proteins are meant to stick to and kill specific bacterial and viral proteins, but sometimes “autoantibodies” react to the body’s “self” proteins and cause autoimmune disease.
The new study, led by investigators at NYU Grossman School of Medicine and its Perlmutter Cancer Center, generated data suggesting that a newly identified panel of autoantibodies if detected in patients’ blood prior to immunotherapy, has the potential to reliably predict whether a patient’s cancer would recur and whether they would experience autoimmune side effects as a result of the treatment itself.
The people in the study had received adjuvant immunotherapy, which is meant to stop cancer from coming back after it has been treated.
Immune cells include “checkpoint” sensors that turn them off when they receive the proper signal, protecting normal cells from autoimmune attack. Tumors are seen as abnormal by the body, but cancer cells use checkpoints, like programmed death receptor 1 (PD-1), to stop the immune system from attacking. As a type of immunotherapy, PD-1 inhibitors are effective against many cancers and are given to people whose melanoma has been removed as a kind of follow-up treatment. However, some patients have chronic illness or serious side effects from their treatments, according to researchers.
The research team hypothesized that certain patients may have greater levels of critical autoantibodies prior to therapy, but not sufficient levels to be diagnosed with an autoimmune disease. They expected that this latent sensitivity would then be activated by checkpoint inhibitors, resulting in more severe immune-based side effects.
With the use of two of the most effective checkpoint inhibitors, nivolumab and ipilimumab, as well as their combination, the team in the current study was able to identify a panel of unique autoantibody signatures that could predict immune-related side effects. Even though their data support the predictive utility of the autoantibody scores by comparing them to data from clinical trials, the researchers say that more research is needed to confirm the value of such a test in the clinic and to better understand the relationship between autoantibodies, recurrence, and toxicity.
The findings “show that the new research test, by predicting whether a patient will respond to a treatment or experience side effects,” according to study first author Paul Johannet, “has the potential to help physicians make more precise treatment recommendations.”
“With further validation,” adds the first author, “this composite panel might help patients to better balance the chances of treatment success against severe side effects.”
Study participants included around 950 people with advanced melanoma who gave blood as part of one of two Phase 3 randomized controlled trials of adjuvant checkpoint inhibitors. Before receiving therapy, these patients had their tumors surgically removed and blood samples taken. The new test uses a microchip on which 20,000 proteins have been strategically placed. When an antibody finds a protein in a blood sample, that protein’s spots light up, and the signal gets stronger as the amount of antibody goes up.
A score-based prediction method for each treatment was created by co-senior author Judy Zhong, PhD, and colleagues utilizing statistical modeling, the newly found panel of autoantibodies, and other data. According to Zhong, patients with a high autoantibody recurrence score experienced a speedier return of their condition than those with a lower score. The risk of developing severe side effects was also considerably higher in patients with higher pre-treatment autoantibody toxicity ratings than in those with lower values.
“That we identified 283 autoantibody signals shows that the biological phenomena underlying recurrence and toxicity are complex, and cannot be driven one or two biomarkers” adds Osman.
Next, the researchers want to see if autoantibody profiles can accurately predict outcomes for patients with the other forms of cancer for which checkpoint inhibitors are licensed.
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