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

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 immu

SUBMITTER: Filipski K 

PROVIDER: S-EPMC8291310 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature