DNA Methylation Profiling in Melanoma: From Lesion Classification to Therapeutic Stratification
Organizations: Division of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), INF 223, 69120 Heidelberg, Germany · Medical Faculty, University Heidelberg, Heidelberg, Germany · Department of Epigenetics, German Cancer Research Center (DKFZ), Heidelberg, Germany · Department of Dermatology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany, Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Dresden, Germany · Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany · Department of Dermatology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg; Comprehensive Cancer Center Erlangen – European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen; Bavarian Cancer Research Center (BZKF), Erlangen, Germany · Department of Dermatology, Venereology and Allergology, Charité – Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany · Vivantes Hospital Spandau, Department of Dermatology and Allergology, Berlin, Germany · Department of Dermatology, University Hospital Regensburg, Regensburg, Germany · Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany · Department of Dermatology, Uniklinikum Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany. · Deutsches Zentrum Immuntherapie (DZI), Uniklinikum Erlangen, Erlangen, Germany. · Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, National Center for Tumor Diseases (NCT) WERA Würzburg and Bavarian Cancer Research Center (BZKF), Würzburg, Germany · Goethe-University Frankfurt, University Hospital, Department of Dermatology, Germany. · Division of Translational Medical Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany · National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany · German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany · Division of Translational Precision Medicine, Institute of Human Genetics, Heidelberg University, Heidelberg, Germany · Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany; CCU Neuropathology, DKTK, DKFZ · Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany · Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany · Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany · Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom · Institute of Pathology, University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany · Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, Mannheim, Germany · Department of Medicine, Division of Hematology/Oncology, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA · Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Harvard Data Science Initiative, Harvard University, Boston, Massachusetts.
Abstract
DNA methylation provides a stable record of cellular identity, capturing epigenetic programs that distinguish specialized cell states despite a shared genome. Because malignant transformation and tumour progression are accompanied by extensive epigenetic remodeling, we hypothesized that the methylome of melanocytic lesions contains biologically and clinically relevant information for both diagnosis and disease progression. In a cohort of 1,001 tissue samples prospectively collected across eight German university hospitals profiled using Illumina Infinium MethylationEPIC arrays, we compared machine-learning models based on selected Cytosine phosphate Guanine (CpG) methylation sites with models incorporating biology-guided features, including epigenetic age acceleration, cell type composition and copy-number variation burden. In an external test set, the best diagnostic classifier was CpG-based and distinguished melanocytic nevi, noninvasive melanoma and invasive melanoma with a macro-averaged area under the receiver operating characteristic curve of 0.919 (95% CI: 0.878 to 0.952). Notably, across CpGs most strongly hyper- and hypomethylated between NV and IM, NIM showed an intermediate methylation profile, providing a molecular correlate of its diagnostic complexity. The best model for clinically relevant treatment group prediction, with AJCC stages grouped according to guideline-based management recommendations, relied on biology-guided features and achieved a macro-averaged mean absolute error of 0.627 (95% CI: 0.477 to 0.808). Together, these findings demonstrate that methylation-based models can capture both diagnostic identity and clinically relevant disease stratification, supporting DNA methylation as a promising biomarker for further validation and potential clinical translation.