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Supplementary Data from Tumor Expression Quantitative Trait Methylation Screening Reveals Distinct CpG Panels for Deconvolving Cancer Immune Signatures

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posted on 2023-03-31, 05:06 authored by Xiaoqing Yu, Ling Cen, Y. Ann Chen, Joseph Markowitz, Timothy I. Shaw, Kenneth Y. Tsai, Jose R. Conejo-Garcia, Xuefeng Wang
Supplementary Data from Tumor Expression Quantitative Trait Methylation Screening Reveals Distinct CpG Panels for Deconvolving Cancer Immune Signatures

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Institutional Research Grant

American Cancer Society

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NIH

Moffitt Skin Cancer SPORE

National Cancer Institute

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H. Lee Moffitt Cancer Center & Research Institute

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ARTICLE ABSTRACT

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. This study provides a novel and promising approach to identify targeted epigenetic biomarkers in cancer and will spur further analysis in tumor immune phenotyping.

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