Open AccessTumor Expression Quantitative Trait Methylation Screening Reveals Distinct CpG Panels for Deconvolving Cancer Immune Signatures
Author(s) -
Xiaoqing Yu,
Ling Cen,
Yian Ann Chen,
Joseph Markowitz,
Timothy I. Shaw,
Kenneth Y. Tsai,
José R. Conejo-Garcia,
Xuefeng Wang
Publication year - 2022
Publication title -
cancer research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.103
H-Index - 449
eISSN - 1538-7445
pISSN - 0008-5472
DOI - 10.1158/0008-5472.can-21-3113
Subject(s) - epigenetics , dna methylation , biology , biomarker , methylation , cancer , computational biology , cpg site , immune system , tumor microenvironment , cancer research , bioinformatics , gene , immunology , gene expression , genetics
DNA methylation signatures in tumors could serve as reliable biomarkers that are accessible in archival tissues for tracking the epigenetic dynamics shaped by both cancer cells and the tumor microenvironment. However, given the ultrahigh dimensionality and noncollapsible nature of the data, it remains challenging to screen all CpG sites to identify the most promising marker panels. In this article, we introduce the concept of tumor-based expression quantitative trait methylation (eQTM) for the prioritization and systematic mining of predictive biomarkers. In melanoma as a disease model, eQTM CpGs and genes represent new and efficient candidate targets to be investigated for both prognostic and immune status monitoring purposes. Three cis-eQTM CpGs (cg07786657, cg12446199, and cg00027570) were strongly associated with and can serve as surrogate biomarkers for the tumor immune cytolytic activity score (CYT). In addition, multiple eQTM genes could be further exploited for predicting immunoregulatory phenotypes. A targeted gene panel analysis identified one eQTM in TCF7 (cg25947408) as a novel candidate biomarker for uncoupling overall T-cell differentiation and exhaustion status in a tumor. The prognostic significance of this eQTM as an independent signature to CYT was validated by both The Cancer Genome Atlas and Moffitt melanoma cohort data. Overall, eQTMs represent a mechanistically distinct class of potential biomarkers that can be used to predict patient prognosis and immune status.