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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.

2022-11-16 | GSE193297 | GEO

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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ProteoMixture: A Cell Deconvolution Tool for Bulk Tissue Proteomics Data

Project description:Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

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Clinically-relevant cell type cross-talk identified from a lung tumor microenvironment interactome

Project description:Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway. To develop a deeper understanding of the interactions between cells within human lung tumors we performed RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We mapped the cell-specific differential expression of prognostically-associated secreted factors and cell surface genes, and computationally reconstructed cross-talk between these cell types to generate a novel resource we call the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identified and validated a prognostically unfavourable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also found a favorable association between infiltration of mast cells and less aggressive tumor cell behavior. These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.

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Deconvolution of whole blood DNA methylomes reveals immune cell type-specific differential methylation in Multiple Sclerosis

Project description:Whole blood is a highly convenient and informative tissue from which to sample DNA and RNA in epigenomic and functional genomic studies, but it is comprised of multiple distinct cell types and this complexity significantly impairs our ability to interpret downstream differential methylation and/or differential expression results. In this multiple sclerosis (MS)-focused study we utilised an application of current statistical deconvolution methods to interrogate whole blood DNA methylation data thereby enabling the methylome of several immune cell types to be analysed independently. Methylome profiling on cell type-purified blood samples revealed optimal CpG sets for use as robust immune cell markers in the statistical deconvolution process. We show that it is possible to identify differentially methylated (DM) loci in a cell type specific manner using statistical deconvolution. Finally, we demonstrate that deconvolution improved the biological relevance and interpretability of our DM results, significantly enhancing concordance of the identified DM loci with loci previously shown to be genetically or epigenetically associated with MS.

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