Dysregulation of gene expression is central to cancer development, and genes do not function in isolation but instead as part of an expansive gene regulatory network. Increasing complexity further, within the tumour is a highly diverse array of cell types. Understanding changes to cell type-specific gene regulatory networks (GRNs) during cancer development can reveal valuable insights with the potential to inform clinical decisions and treatments.
Cell type-specific GRNs can be reconstructed from single-cell RNA sequencing data by existing bioinformatic tools, however single cell data is often not available in the clinic for large sample sizes. Reconstructing GRNs from larger datasets increases statistical power which will also enable the analysis of GRNs in cancer subtypes beyond the most prevalent. Our research goal is to develop a tool to generate cell type-specific GRNs from bulk RNA-seq data.
Cell type-specific gene expression was predicted from bulk RNA-seq data by cell deconvolution with BayesPrism, followed by sample-specific GRN reconstruction with PANDA and LIONESS. To develop the method, a pseudobulk was created from single-cell RNA-seq data from a publicly available prostate cancer dataset (GSE141445) for the analysis. This allowed benchmarking of the cell-type specific GRNs to those reconstructed with cell types identified from the original single-cell RNA-seq data. Preliminary results show a strong correlation of the network metrics between predicted and real networks (Pearson's test, median r = 0.64). Future research will focus on applying the tool to additional cancer datasets to identify cell type-specific features of GRN dysregulation in the tumour environment.

Co-authors: Mario Hervas, Ine Bonthuis, Tatiana Belova, Daniel Osorio, Ladislav Hovan, Mariike Kuijjer