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.

为了能够在其自然肿瘤微环境中对癌细胞内的调节网络进行计算分析，我们开发了一种两阶段组织表生分析方法。第一阶段涉及迭代计算反卷积，以估计目标基因的样品特异性癌细胞固有表达。第二阶段将基因置于网络模块中。我们在模拟实验中验证了该方法，显示了相对于大量样品的差异表达分析而言改进的性能，并将其应用于阐明间皮素（MSLN）网络在胰腺导管腺癌（PDAC）中的作用。网络分析和随后在一组PDAC细胞系中进行的实验验证表明，MSLN通过两种已知的激活剂激活了AKT，视黄酸受体γ（RARG）和酪氨酸激酶非受体2（TNK2）。综上所述，这些结果证明了组织表生分析的潜力，可以直接从患者肿瘤图谱中揭示癌细胞特异性分子相互作用。