Abstract 3141: Epiregulon infers single-cell transcription factor activity to dissect mechanisms of lineage plasticity and drug response

Abstract

Abstract Single-cell multiomic technologies enable holistic reconstruction of cell states and hold the potential to identify drivers of tumor evolution and drug resistance. However, the data sparsity of single-cell assays can preclude accurate estimation of transcription factor activity. In addition, gene expression alone cannot capture transcription factor activity, especially in the context of drug treatment which alters transcription factor function without suppressing gene expression. To circumvent these challenges, we have developed epiregulon, a R package that constructs gene regulatory networks and infers transcription factor (TF) activity in single cells by integrating single cell gene expression, chromatin accessibility and bulk TF occupancy data. Epiregulon applies tests of independence to identify likely transcription factor - regulatory element - target gene relationships occurring at joint probabilities exceeding the expected probabilities of independent events. With epiregulon, we are able to detect lineage factor activity at enhanced sensitivity and predict perturbations more accurately than gene expression could. We further applied this tool to understand transcription factor activity to enzalutamide treatment and identify potential drivers of enzalutamide resistance. Finally, we generated a ground truth dataset using reprogram-seq, a technology that captures multi-omic profiles of single cells upon expression of defined transcription factors. Epiregulon correctly predicts NKX2-1 targets and demonstrates that NKX2-1 expression reprograms the epigenome towards a neuroendocrine state in LNCaP cells. Epiregulon is a useful tool that constructs gene regulatory networks to model the underlying gene regulation hierarchies that drive gene expression and cell states. Citation Format: Tomasz Wlodarczyk, Jenille Tan, Kerstin Seidel, Diana Wu, Shang-Yang Chen, Aleksander Chlebowski, Timothy Keyes, Yu Guo, Aaron Lun, Christopher Siebel, Shiqi Xie, Xiaosai Yao. Epiregulon infers single-cell transcription factor activity to dissect mechanisms of lineage plasticity and drug response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3141.