DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma

被引：33

作者：

Filipski, Katharina ^{[1
,2
,3
]}

Scherer, Michael ^{[4
,5
,6
]}

Zeiner, Kim N. ^{[7
]}

Bucher, Andreas ^{[8
]}

Kleemann, Johannes ^{[7
]}

Jurmeister, Philipp ^{[9
,10
,11
,12
,13
]}

Hartung, Tabea, I ^{[1
]}

Meissner, Markus ^{[7
]}

Plate, Karl H. ^{[1
,2
,3
]}

Fenton, Tim R. ^{[14
]}

Walter, Jorn ^{[4
]}

Tierling, Sascha ^{[4
]}

Schilling, Bastian ^{[15
]}

Zeiner, Pia S. ^{[2
,3
,16
]}

Harter, Patrick N. ^{[1
,2
,3
]}

机构：

[1] Univ Hosp, Neurol Inst, Edinger Inst, Frankfurt, Germany

[2] German Canc Res Ctr, German Canc Consortium DKTK Heidelberg, Heidelberg, Germany

[3] Frankfurt Canc Inst FCI, Frankfurt, Germany

[4] Univ Saarland, Dept Genet, Saarbrucken, Germany

[5] Max Planck Inst Informat, Saarland Informat Campus, Saarbrucken, Germany

[6] Grad Sch Comp Sci, Saarland Informat Campus, Saarbrucken, Germany

[7] Univ Hosp, Dept Dermatol, Frankfurt, Germany

[8] Univ Hosp, Dept Radiol, Frankfurt, Germany

[9] Charite Univ Med Berlin, Inst Pathol, Berlin, Germany

[10] Free Univ Berlin, Berlin, Germany

[11] Humboldt Univ, Berlin, Germany

[12] Berlin Inst Hlth, Berlin, Germany

[13] Ludwig Maximilians Univ Hosp Munich, Inst Pathol, Munich, Germany

[14] Univ Kent, Sch Biosci, Canterbury, Kent, England

[15] Univ Hosp Wurzburg, Dept Dermatol, Wurzburg, Germany

[16] Univ Hosp, Dr Senckenberg Inst Neurooncol, Frankfurt, Germany

来源：

JOURNAL FOR IMMUNOTHERAPY OF CANCER | 2021年 / 9卷 / 07期

基金：

欧盟地平线“2020”;

关键词：

biostatistics; immunotherapy; melanoma; tumor biomarkers; biomarkers; tumor; SURVIVAL; IPILIMUMAB; PEMBROLIZUMAB; NIVOLUMAB; IMMUNOTHERAPY; EXPRESSION; OUTCOMES;

D O I：

10.1136/jitc-2020-002226

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

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.

引用

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