Immune checkpoint inhibitors (ICI) have improved clinical outcome for many patients with advanced cutaneous malignant melanoma (CMM) during the last decade. The magnitude and duration of response vary considerably between patients. Predictive biomarkers to identify patients that will benefit from treatment can increase efficacy, diminish side effects and costs. Our aim is to identify predictive biomarkers in tumors samples from patients with advanced CMM receiving ICI.
Patients with advanced CMM at Karolinska University Hospital, Stockholm, starting therapy with ICI were invited to participate in our study. After signing informed consent a pre-treatment fine needle or core biopsy was taken from an accessible metastasis. RNA was extracted to perform targeted RNA sequencing using the Ion AmpliSeq Transcriptome Human Gene Expression Kit for RefSeq genes. Partek Genomics Suite® software was applied to find differentially expressed genes and correlate the data with therapy response and progression free survival (PFS).
Nineteen patients were included between September2013 and August2017, 8 female and 11 male. The median age was 70 years old (range 49 – 84). All patients had metastatic disease (13 M1c, 4 M1b, 2 M1a). ICI was first-line treatment for 16 patients. Nivolumab or pembrolizumab was given to 17 patients and two received ipilimumab. Five patients had partial response, 3 stable disease and 7 complete response whereas 4 had progressive diasease. Median PFS was 10 months (range 1,2 – 62 months, 6 patients still responding). High expression of a subset of genes playing a role for DNA replication, genes involved in chromatin remodeling and cell cycle were significantly associated with shorter PFS. The correlation between low expression of interferon gamma signature genes and poorer treatment outcome was confirmed in our study.
Our findings suggest that genes involved in the regulation of DNA replication, chromatin remodeling and cell cycle may influence the long-term response to ICI.
Clinical trial identification
Legal entity responsible for the study
Karolinska University Hospital and Karolinska Institute.
The Swedish Cancer Society, the Cancer Research Funds of Radiumhemmet and Knut and Alice Wallenberg foundation. We aknowledge support of the Science for Life Laboratory, National Genomics Infrastructure (NGI)/Uppsala, Genome Center and UPPMAX for providing assistance in massive parallel sequencing and computational infrastructure (work funded by RFI/VR and Scilife, Sweden).
All authors have declared no conflicts of interest.