DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma. Issue 7 (19th July 2021)

• Record Type: Journal Article
• Authors: Filipski, Katharina; Scherer, Michael; Zeiner, Kim N.; Bucher, Andreas; Kleemann, Johannes; Jurmeister, Philipp; Hartung, Tabea I.; Meissner, Markus; Plate, Karl H.; Fenton, Tim R.; Walter, Jörn; Tierling, Sascha; Schilling, Bastian; Zeiner, Pia S.; Harter, Patrick N.
• Is Part Of: Journal for immunotherapy of cancer. Volume 9:Issue 7(2021)
• Publication Date: 2021-07-19
• Subjects: biostatistics -- immunotherapy -- melanoma -- tumor biomarkers -- biomarkers -- tumor; Cancer -- Immunotherapy -- Periodicals; Cancer -- Immunological aspects -- Periodicals; Tumors -- Immunological aspects -- Periodicals; Immunotherapy -- Periodicals; 616.99406105
• DOI: 10.1136/jitc-2020-002226
• Languages: English
• ISSNs: 2051-1426

Abstract

Abstract : 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.