DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma

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

Combination of Immune Checkpoint Inhibitors and Liver-Specific Therapies in Liver-Metastatic Uveal Melanoma: Can We Thus Overcome Its High Resistance?

Uveal Melanoma (UM) is a rare disease; however, it is the most common primary intraocular malignant tumor in adults. Hematogenous metastasis, occurring in up to 50% of cases, mainly to the liver (90%), is associated with poor clinical course and treatment failure. In contrast to dramatic benefits of immunotherapy in many tumor entities, as seen in cutaneous melanoma, immune checkpoint inhibitors (ICI) do not achieve comparable results in Metastatic UM (MUM). The aim of this study was to investigate whether the combination of ICI with liver-directed therapies provides a potential survival benefit for those affected. This retrospective, single-center study, including n = 45 patients with MUM, compared the effect of combining ICI with liver-directed therapy (“Cohort 1”) with respect to standard therapies (“Cohort 2”) on overall survival (OS). Our results revealed a significant survival difference between Cohort 1 (median OS 22.5 months) and Cohort 2 (median OS 11.4 months), indicating that this combination may enhance the efficacy of immunotherapy and thus provide a survival benefit. There is an urgent need for randomized, prospective trials addressing the combination of liver-directed therapies and various strategies of immunotherapy (such as ICI; IMCgp100; personalized vaccines) in order to establish regimens which finally improve the prognosis of patients with MUM

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma

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