DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma
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DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma. / 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.
In: J IMMUNOTHER CANCER, Vol. 9, No. 7, 07.2021.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma
AU - Filipski, Katharina
AU - Scherer, Michael
AU - Zeiner, Kim N
AU - Bucher, Andreas
AU - Kleemann, Johannes
AU - Jurmeister, Philipp
AU - Hartung, Tabea I
AU - Meissner, Markus
AU - Plate, Karl H
AU - Fenton, Tim R
AU - Walter, Jörn
AU - Tierling, Sascha
AU - Schilling, Bastian
AU - Zeiner, Pia S
AU - Harter, Patrick N
N1 - © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2021/7
Y1 - 2021/7
N2 - 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.
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.
KW - DNA Methylation/genetics
KW - Female
KW - Humans
KW - Immune Checkpoint Inhibitors/pharmacology
KW - Immunotherapy/methods
KW - Male
KW - Melanoma/drug therapy
U2 - 10.1136/jitc-2020-002226
DO - 10.1136/jitc-2020-002226
M3 - SCORING: Journal article
C2 - 34281986
VL - 9
JO - J IMMUNOTHER CANCER
JF - J IMMUNOTHER CANCER
SN - 2051-1426
IS - 7
ER -
