Drug treatment

Transcription factors (TFs) and transcriptional coregulators represent an emerging and exciting class of targets. By quantifying target gene modulation, gene regulatory networks (GRNs) delineate disease biology and evaluate pharmacological agents targeting these regulators. However, none of the existing methods are explicitly designed to measure the effects of perturbations in which TF expression is decoupled from its activity. We present Epiregulon, a method that constructs GRNs from single-cell ATAC and RNA data for accurate prediction of TF activity. Our weight estimation, based on co-occurrence of TF expression and chromatin accessibility, avoids erroneous inflation of TF activity as seen with TF expression only approaches. Furthermore, our utilization of ChIP-seq data expands inference to include transcriptional coregulators lacking defined motifs. Our extensive network of regulators facilitates identification of cell-state specific interaction partners. Using Epiregulon, we uncover divergent cell fate transitions of prostate cancer cells driven by NKX2-1 and GATA6 expression. We accurately predicted the effects of AR inhibition across various drug modalities. Finally, Epiregulon was able to infer the context-dependent activity of a chromatin remodeler lacking a defined motif sequence and recapitulate the unique etiologies of prostate cancer. By mapping out the network of key regulators across a multitude of perturbations, Epiregulon can accelerate the discovery of new therapeutics targeting transcription factors.