TY - JOUR T1 - DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma JF - Journal for ImmunoTherapy of Cancer JO - J Immunother Cancer DO - 10.1136/jitc-2020-002226 VL - 9 IS - 7 SP - e002226 AU - Katharina Filipski AU - Michael Scherer AU - Kim N. Zeiner AU - Andreas Bucher AU - Johannes Kleemann AU - Philipp Jurmeister AU - Tabea I. Hartung AU - Markus Meissner AU - Karl H. Plate AU - Tim R. Fenton AU - Jörn Walter AU - Sascha Tierling AU - Bastian Schilling AU - Pia S. Zeiner AU - Patrick N. Harter Y1 - 2021/07/01 UR - http://jitc.bmj.com/content/9/7/e002226.abstract N2 - 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.Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as supplementary information. All data analyzed and/or generated within this study are included in the article and the supplementary data files. Raw data IDAT files as well as processed data of the ICI cohort are accessible via Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ; GSE175699). Any other relevant data are available upon reasonable request. ER -