Uveal melanoma (UM) is highly refractory to treatment with dismal prognosis in advanced stages. The value of the combined checkpoint blockade with CTLA-4 and PD-1 inhibition in metastatic UM is currently unclear. Patients with metastatic or unresectable UM treated with ipilimumab in combination with a PD-1 inhibitor were collected from 16 German skin cancer centers. Patient records of 64 cases were analyzed for response, progression-free survival (PFS), overall survival (OS), and safety. Clinical parameters and serum biomarkers associated with OS and treatment response were determined with Cox regression modelling and logistic regression. The best overall response rate to combined checkpoint blockade was 15.6% with 3.1 and 12.5% complete and partial response, respectively. The median duration of response was 25.5 months (range 9.0–65.0). Stable disease was achieved in 21.9%, resulting in a disease control rate of 37.5% with a median duration of the clinical benefit of 28.0 months (range 7.0–65.0). The median PFS was 3.0 months (95% CI 2.4–3.6). The median OS was estimated to 16.1 months (95% CI 12.9–19.3). Regarding safety, 39.1% of treated patients experienced a severe, treatment-related adverse event according to the CTCAE criteria (grade 3: 37.5%; grade 4: 1.6%). The most common toxicities were colitis (20.3%), hepatitis (20.3%), thyreoiditis (15.6%), and hypophysitis (7.8%). A poor ECOG performance status was an independent risk factor for decreased OS (p = 0.007). The tolerability of the combined checkpoint blockade in UM may possibly be better than in trials on cutaneous melanoma. This study implies that combined checkpoint blockade represents the hitherto most effective treatment option available for metastatic UM available outside of clinical trials.

Background Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. Methods A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). Results We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. Conclusions These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.