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.
Highlights
The study of oncogene expression in unperturbed cancer cells within their tumor microenvironment provides insights that may not be gained from other more accessible models that may not reflect as closely the tumor biology in vivo
One obvious limitation of Epigenomic Deconvolution (EDec) is the requirement that the two groups be predefined. This limitation precludes grouping by cellintrinsic expression of genes of interest such as MSLN. To address this “chicken and egg” problem, we develop a histoepigenetic analysis method that expands on the EDec method in two ways: (1) It integrates within an iterative procedure a two-way clustering step with deconvolution to separate pancreatic ductal adenocarcinoma (PDAC) tumors based on cancer-cell-specific expression of a gene of interest such as MSLN; (2) It estimates a network of genes potentially interacting with the gene of interest
By eliminating artifacts associated with physical separation of cells and their propagation in vitro, the method produces hypotheses that are testable in tractable models such as cell lines while ensuring relevance of the knowledge gained for human tumor biology in vivo
Summary
The study of oncogene expression in unperturbed cancer cells within their tumor microenvironment provides insights that may not be gained from other more accessible models that may not reflect as closely the tumor biology in vivo. One illustrative example is the glycosylphosphatidylinositol-anchored cell-surface protein mesothelin (MSLN), whose expression is elevated in while its interaction with MUC16 contributes to cell motility and invasion[6,7]. These models have shown limited utility for developing therapies targeting MSLN: while preclinical studies targeting MSLN in pancreatic cancer cell lines and patient-derived xenograft mouse models have resulted in inhibition of cancer growth[8,9], early-stage clinical trials have shown stabilized disease or increase in survival for only a few patients[10,11], with partial and overall responses being mostly low or absent[12,13].
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