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HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor‑type‑specific DNA methylation data

ABSTRACT: HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor‑type‑specific DNA methylation data

PROVIDER: PRJNA795686 | ENA |

REPOSITORIES: ENA

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HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor‑type‑specific DNA methylation data

Project description:The solid tumor microenvironment is heterogeneous and varies in composition by tumor type. High-resolution profiling of the cell composition in tumor microenvironments is crucial to understanding its biological role and function in clinical outcomes. Previously, gene expression and DNA methylation-based deconvolution approaches for tumor microenvironment have succeeded in deconvolving major cell types. However, existing methods lack accuracy and specificity to tumor type and include a limited amount of cell types. We developed a novel DNA methylation-based algorithm, HiTIMED, to estimate cell proportions in tumor microenvironments with a high resolution. HiTIMED employs a tumor-site-specific hierarchical model to enhance the accuracy of the predictions. Seventeen cell types, three major tumor microenvironment components (tumor, immune, angiogenic), and twenty carcinoma types can be profiled by HiTIMED. We demonstrated prognostic significance of HiTIMED cell types that other methods in the tumor microenvironment deconvolution were not capturing. The high resolution of HiTIMED deconvolution provides additional opportunities to study the impact of the tumor microenvironment on clinically significant outcomes like immunotherapy response.

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Project description:MeDIP-seq is an enrichment-based DNA methylation profiling technique that measures abundance of methylated DNA. While this technique offers efficiency advantages over direct methylation profiling, it does not provide absolute quantification of DNAm necessary for cell type deconvolution. We introduce decemedip, a Bayesian hierarchical model for cell type deconvolution of methylated sequencing data that leverages reference atlases derived from direct methylation profiling. We demonstrate its accuracy and robustness through simulation studies and validation on cross-platform measurements, and highlight its utility in identifying tissue-specific and cancer-associated methylation signatures using MeDIP-seq profiling of patient-derived xenografts and cell-free DNA. decemedip is available at \url{https://github.com/nshen7/decemedip}.

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HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data.

Project description:BackgroundCellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types.ResultsWe employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture.ConclusionWe developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.

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