Histoepigenetic analysis of the mesothelin network within pancreatic ductal adenocarcinoma cells reveals regulation of retinoic acid receptor gamma and AKT by mesothelin

Abstract

To enable computational analysis of regulatory networks within the cancer cell in its natural tumor microenvironment, we develop a two-stage histoepigenetic analysis method. The first stage involves iterative computational deconvolution to estimate sample-specific cancer-cell intrinsic expression of a gene of interest. The second stage places the gene within a network module. We validate the method in simulation experiments, show improved performance relative to differential expression analysis from bulk samples, and apply it to illuminate the role of the mesothelin (MSLN) network in pancreatic ductal adenocarcinoma (PDAC). The network analysis and subsequent experimental validation in a panel of PDAC cell lines suggests AKT activation by MSLN through two known activators, retinoic acid receptor gamma (RARG) and tyrosine kinase non receptor 2 (TNK2). Taken together, these results demonstrate the potential of histoepigenetic analysis to reveal cancer-cell specific molecular interactions directly from patient tumor profiles.

Proteogenomic characterization of pancreatic ductal adenocarcinoma

“…To ensure that the second matrix reflects the cell type composition, EDec uses only the methylation features (i.e., probe-level methylation) that are known to have differential levels across the presumed cell types within the tumor. We selected such methylation features from cell lines or physically purified tissues that are available in the public database (Table S6; adapted from Lurie et al, 2020). Based on the robustness of NMF matrix decomposition, the methylation-based deconvolution resulted into four cell types: tumor epithelial cells, immune cells, stromal cells and mature exocrine and endocrine cells.…”

ZNF718, HOXA4, and ZFP57 are differentially methylated in periodontitis in comparison with periodontal health: Epigenome‐wide DNA methylation pilot study

“…DropMethylationLoci function of minfi package was used for filtering sex chromosome probes, CH dinucleotides, and any common single polymorphisms (SNPs) 39,40 ; in addition, as a complement for this filtering, the annotation-file EPIC.hg19.manifest was obtained from http://zwdzwd.io/Infin iumAn notat ion/curre nt/EPIC as with other studies 41 to control for a potential bias due to genetic variants.…”

XDec-CHI reveals immunosuppressive interactions in pancreatic ductal adenocarcinoma

“…In addition to the package, we have released a web application to enable reproducibility of our findings and empower the community to perform cell-type-specific analysis without running the R package. The web application hosts provide correlation analyses based on pre-computed deconvolution of five cancer types (PDAC, BRCA, HNSC, GBM, and Glioma) and are accessible at https://brl-bcm.shinyapps.io/XDec-CHI_Homepage ( Carrero et al., 2019 ; Lucero et al., 2020 ; Lurie et al., 2020 ; Murillo et al., 2022 ).…”

“…It also cannot be applied to standard FFPE samples collected in clinical practice. To address this issue, computational deconvolution methods that estimate tumor composition from bulk methylation or RNA-seq profiles have been developed ( Carter et al., 2012 ; Chan-Seng-Yue et al., 2020 ; Lurie et al., 2020 ; Newman et al., 2019 ; Onuchic et al., 2016 ; Peng et al., 2019b ). The most recent generation of such tools, including the highly popular CIBSERSORTx, utilize single-cell RNA-seq (scRNA-seq) information collected on a small number of samples to create cell-type-specific reference profiles that are subsequently utilized for computational deconvolution of bulk RNA-seq profiles ( Newman et al., 2019 ; Peng et al., 2021 ; Wang et al., 2019 ).…”

“…The strategy is particularly suitable for situations where bulk profiling data is available for a much larger number of tumors than single-cell data, implying that the diversity of tumor types is better represented in bulk profiles. The reference-optional strategy, initially implemented by the EDec algorithm, has indeed shown the potential to recover the diversity of cancer cell states that correspond to clinically relevant subtypes of breast cancer ( Onuchic et al., 2016 ), glioblastoma ( Lucero et al., 2020 ) and pancreatic ductal adenocarcinoma ( Lurie et al., 2020 ).…”