RT Journal Article
SR Electronic
T1 DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma
JF Journal for ImmunoTherapy of Cancer
JO J Immunother Cancer
FD BMJ Publishing Group Ltd
SP e002226
DO 10.1136/jitc-2020-002226
VO 9
IS 7
A1 Katharina Filipski
A1 Michael Scherer
A1 Kim N. Zeiner
A1 Andreas Bucher
A1 Johannes Kleemann
A1 Philipp Jurmeister
A1 Tabea I. Hartung
A1 Markus Meissner
A1 Karl H. Plate
A1 Tim R. Fenton
A1 Jörn Walter
A1 Sascha Tierling
A1 Bastian Schilling
A1 Pia S. Zeiner
A1 Patrick N. Harter
YR 2021
UL http://jitc.bmj.com/content/9/7/e002226.abstract
AB 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.