Researchers have identified key markers to help physicians predict whether or not a patient may respond positively to a type of melanoma therapy, according to a study published by Nature Communications in September.
Immune checkpoint inhibitor (ICI) therapy is currently the most efficient treatment for those with metastatic melanoma—which spreads cancer cells from the primary tumor to other areas of the body—demonstrating durable remission in up to half of patients with advanced melanoma. The therapy works by blocking proteins on tumor or immune cells that prevent the immune system from killing cancer cells.
Immune checkpoints are normally present to prevent an immune response from being so strong that it destroys healthy cells in our bodies. When immune cells identify tumor cells, immune checkpoints can engage to stop the immune system from destroying the cancer. Immune checkpoint inhibitor drugs work by blocking checkpoint proteins, allowing immune cells to kill cancer cells. ICI-based therapy is thus a type of immunotherapy.
However, there are no reliable indicators to help physicians predict a patient’s response to ICI-based therapy. While there is one FDA-approved biomarker for ICI melanoma treatment, the tumor mutation burden biomarker, the mechanisms linking it to ICI remain unclear.
The new study, led by researchers Noam Auslander and Andrew Patterson, is thus a breakthrough in predicting and improving outcomes of ICI melanoma therapy.
The scientists found that mutations in white blood cells (leukocytes) and T-cell proliferation regulation have the potential to be biomarkers with reliable and stable prediction of ICI therapy response.
“This work aims to identify better and more biologically interpretable genomic predictors for immunotherapy responses,” said Auslander, assistant professor in the Molecular and Cellular Oncogenesis Program of Wistar’s Ellen and Ronald Caplan Cancer Center. “We need better biomarkers to help select patients that are more likely to respond to ICI therapy and to understand what factors can help to enhance responses, and increase those numbers.”
The ability to predict how patients may respond to ICI therapy will reduce the number of those who have a severe adverse autoimmune response to treatment.
Furthermore, Patterson stated that findings from the study will help scientists “extract biological information that could help in further understanding the mechanisms behind ICI therapy response and resistance.”