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
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.
show moreshow less

Download full text files

Export metadata

  • Export Bibtex
  • Export RIS
Metadaten
Author:Katharina Johanna Filipski, Michael Scherer, Kim Nikola Zeiner, Andreas Bucher, Johannes Kleemann, Philipp Jurmeister, Tabea I. Hartung, Markus Meissner, Karl Plate, Tim R. Fenton, Jörn Walter, Sascha Tierling, Bastian Schilling, Pia S. Zeiner, Patrick Nikolaus Harter
URN:urn:nbn:de:hebis:30:3-644275
DOI:http://dx.doi.org/10.1136/jitc-2020-002226
ISSN:2051-1426
Parent Title (English):Journal for ImmunoTherapy of Cancer
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Language:English
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
Volume:9
Issue:e002226
Pagenumber:20
First Page:1
Last Page:11
Note:
 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”.
HeBIS PPN:49205164X
Institutes:Medizin
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Open-Access-Publikationsfonds:Medizin
Licence (English):License LogoCreative Commons - Namensnennung-Nicht kommerziell 4.0

$Rev: 11761 $