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.

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Author:Katharina Johanna FilipskiGND, Michael Scherer, Kim Nikola ZeinerGND, Andreas Bucher, Johannes KleemannORCiDGND, Philipp Jurmeister, Tabea Isabelle HartungGND, Markus MeissnerORCiDGND, Karl PlateGND, Tim R. FentonORCiD, Jörn Walter, Sascha Tierling, Bastian SchillingORCiDGND, Pia Susan ZeinerORCiD, Patrick Nikolaus HarterORCiDGND
Parent Title (English):Journal for ImmunoTherapy of Cancer
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Date of Publication (online):2021/07/19
Date of first Publication:2021/07/19
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/03/07
Page Number:20
First Page:1
Last Page:11
KF has received intramural funding by the Frankfurt Research Funding (FFF) program "Nachwuchswissenschaftler" and the "Clinician Scientist Program" by the Mildred-Scheel Foundation. MS is supported by the BMBF project de.NBI-epi (031L0101D) and the EU H2020 project SYSCID (733100). PSZ has received intramural funding by the FFF program "Nachwuchswissenschaftler" and "Patenschaftsprogramm" as well as within the "Clinician Scientist Program" by the Mildred-Scheel Foundation. The Dr Senckenberg Institute of Neurooncology is supported by the Dr Senckenberg Foundation. For this study, PNH obtained grants from “FCI/LOEWE: Discovery and Development Program”.
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (English):License LogoCreative Commons - Namensnennung-Nicht kommerziell 4.0