A newly developed scoring system could help identify melanoma patients who may benefit the most from treatment with a type of immunotherapy known as a checkpoint inhibitor, a study finds.
The study, “Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma,” was published in Nature Medicine.
Therapeutic agents, such as checkpoint inhibitors, that can enhance immune cells’ response against cancer cells have provided remarkable clinical gains and been very successful in the treatment of melanoma. However, the effectiveness of the treatment is, to some extent, patient-dependent, and not all patients respond to these therapies.
Immune checkpoint inhibitors are a type of targeted immunotherapy that can block proteins that stop the immune system from attacking cancer cells.
“Being able to predict who is highly likely to respond [to treatment with immune checkpoint inhibitors] and who isn’t will enable us to more accurately and precisely guide patients’ treatment,” Eytan Ruppin, MD, PhD, a researcher at the National Cancer Institute (NCI) and senior author of the study, said in a press release.
To address this, a team of researchers in the U.S. developed an immuno-predictive score (IMPRES) that allows them to distinguish between melanoma patients who may respond to checkpoint inhibitors and those less likely to respond.
To develop their predictive tool, the team looked for clues in cases where the immune system develops an unprompted, successful immune response to cancer, leading to spontaneous tumor regression.
They analyzed neuroblastoma — a type of brain cancer that usually undergoes spontaneous regression in young children — in 108 patients who had either spontaneously regressing or high-risk progressing cancer.
This allowed them to identify gene expression features that separated patients with non-regressing disease from those with regressing disease, and build the IMPRES scoring system. Gene expression is the process by which information in a gene is synthesized to create a working product, such as a protein.
The system uses scores ranging from zero to 15, in which the higher score predicts spontaneous cancer regression and is associated with higher overall patient survival.
Researchers then applied IMPRES to independent data sets of melanoma patients to see if it could predict responses in these patients.
They found that the system was able to identify immune response patterns across different subsets of melanoma cases. A total of 297 samples from melanoma patients, including those who had been treated with checkpoint inhibitors anti-CTLA-4, anti-PD1, or a combination of the two, from several different studies were analyzed. The scoring system was able to identify, with a 77-96% accuracy, those patients who did respond to treatment.
These results become even more relevant when compared with other available predictive strategies, which hold a much lower predictive accuracy ranging from approximately 30-80%.
While these data expand knowledge on how immune checkpoint inhibitors work, it may also open new avenues to help physicians choose the best treatment strategy for patients.
“We now know that immunotherapy works, but we do not understand well why a particular therapy will work for some patients but not others,” said Tom Misteli, PhD, director of the Center for Cancer Research at NCI. “This study is a step forward in developing tools to address this challenge, which is of practical importance to patients.”
Additional studies are still warranted to carefully evaluate the predictive potential of IMPRES in other cancer types that can be treated with checkpoint inhibitors, Ruppin said.
