PD-1 is a checkpoint to slow down T-cells. PD-1  (red) extends from the surface of a T-cell interacting with the ligand protein PD-L1 (yellow) from an antigen presenting cell.
Credit: elvanegra/Getty Images

Responses to the immunotherapy PD-1 checkpoint blockade in patients with advanced melanoma was tied to whether they had received a previous immunotherapy, CTLA-4 blockade, as well as other factors according to research published this week in Cancer Cell.

The findings, by researchers at UCLA Jonsson Comprehensive Cancer Center, was based on the analysis of seven datasets that have been collected of the past ten and included the results of tumor biopsies of more than 500 patients.

“In our large set of data, features that have been used to predict response to anti-PD-1 checkpoint blockade therapy—often called biomarkers—related to the presence of certain immune cell types in the tumor and the genetic profile of the tumors themselves were modified by a patient’s treatment history,”  said lead author Katie Campbell, PhD, a postdoctoral fellow in hematology/oncology at UCLA Jonsson Comprehensive Cancer Center.

Checkpoint inhibitor therapy works by blocking proteins that in response diminish the effectiveness of T cells as a means to boost the body’s immune response to cancer. Patients with advanced melanoma are often treated with immune therapies that have this effect, such as PD-1 blockade and CTLA-4 blockade either alone or in combination.

“As translational scientists, when we work with clinicians, one of the goals is to think about how biomarkers can be used to inform clinical benefit. If we can predict which patients are or are not going to respond to therapy from studying their biopsies, we can start to more strategically define which therapies or combinations of therapies should be used and when,” said co-senior author Antoni Ribas, MD, director of the Tumor Immunology Program at UCLA Jonsson Comprehensive Cancer Center and the Parker Institute for Cancer Immunotherapy Center at UCLA. “Since the current treatment paradigm for melanoma involves combinations or sequential use of immune checkpoint therapies, our study supports how these therapies may work together to effectively treat melanoma. It also highlights the importance of a patient’s prior treatment history as a modifying factor to consider when planning a treatment strategy.”

The work by the UCLA-led team sought to address a gap in biomarker studies related to cancer immunotherapy for late-stage cancers, namely, the fact that much of the information derives from small numbers of samples. To overcome this, the research team assembled and harmonized a large dataset of tumor and clinical data from patients with melanoma in order to identify which factors are indicative of treatment response.

The data collection and harmonization required the varied skill sets of experts in computer science, informatics, statistics, biology, and immunotherapy. The goal of this work was to provide a research resource that can be used to identify statistically significant correlates of anti-PD-1 therapy response, noted Campbell.

“As we performed the analyses, the greatest differences were seen when we accounted for a patient’s prior treatment with anti-CTLA-4 blockade,” Campbell added. “The context in which a biopsy is collected needs to be considered to better define how biomarkers should be implemented in the clinical setting.”

The researchers said that by processing the DNA and RNA sequencing data from hundreds of patients on one cohesive pipeline, they were able to control for some of the wide range of differences that exist across patients, tumors, and treatment histories. They also considered clinical demographics that may be important for understanding why a patient did or did not respond to anti-PD-1 blockade therapy.

While the new research does not provide a direct link to understand when to apply biomarker information for treatment selection, it does provide a foundational dataset and roadmap.

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