Abstract 1212: ExTIME: Extended tumor immune micro-environment cell mixture deconvolution using DNA methylation and a novel tumor-site-specific hierarchical approach

Abstract

Abstract Background: The solid tumor microenvironment is heterogeneous and varies in composition by tumor type. Previous gene expression and DNA methylation deconvolution approaches for tumor micro-environment have had some success for major cell types. However, existing methods lack specificity to tumor type and detailed cell types. We developed 21 tumor-specific DNA methylation-based libraries. We employed a novel hierarchical approach in 3 major tumor microenvironment components (tumor, angiogenic, immune) to profile 17 cell types (see methods below). Methods: DNA methylation data on tumor samples (n=6183) and normal control samples (n=689) for 21 tumor sites were downloaded from GEO and TCGA to develop tumor-type-specific libraries. The top 1000 most informative differentially methylated CpG (DMC) sites were identified using InfiniumPurify for 21 tumor types to project tumor cell proportion. Epithelial, endothelial, and stromal cell samples were used to identify DMCs to profile the cells in the angiogenic environment. Basophil, eosinophil, neutrophil, dendritic cell, monocyte, B naïve, B memory, CD4T naïve, CD4T memory, CD8T naïve, CD8T memory, T regulatory, and natural killer cells were used to identify DMCs to deconvolve the immune environment. In conjunction with the constrained projection/quadratic programming approach, a novel hierarchical approach was employed with six layers and 12 libraries per tumor type to project cell proportions in first, tumor, second, angiogenic, and third, immune micro-environments. The method was validated using purified samples and experimental artificial mixtures. Results: 12 libraries were developed per tumor site to deconvolve 17 cell types in 21 tumors. A preliminary application of the method on TCGA data investigating the association between angiogenic cells and survival revealed worse survival outcomes with a higher proportion of angiogenic cell proportions in BLCA (p<0.01) and HNSC (p=0.02), a higher endothelial cell proportion in CESC (p=0.04), a higher epithelial cell proportion in COAD (p=0.02), a lower endothelial proportion in KIRC (p<0.01), and a lower epithelial proportion in LUAD (p=0.04). Further analyses will be done to investigate the angiogenic and immune microenvironments with prognosis across tumor sites. Conclusion: We developed a DNA methylation-based algorithm, ExTIME, to estimate cell proportions in the tumor microenvironments. This novel approach increased the specificity and accuracy of cell projection by employing a tumor-site-specific hierarchical model. Furthermore, the ExTIME profiles the tumor microenvironment to the most granular level compared to the existing methods. ExTIME’s capability of depicting the cellular composition in tumors promises a better understanding of the cell heterogeneity and its relationship with prognosis across cancers. Citation Format: Ze Zhang, Brock C. Christensen, Lucas A. Salas. ExTIME: Extended tumor immune micro-environment cell mixture deconvolution using DNA methylation and a novel tumor-site-specific hierarchical approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1212.