Yauch, R. | Search bioRxiv

bioRxiv2024

Androgen receptor-negative prostate cancer is vulnerable to SWI/SNF-targeting degrader molecules

Proteolysis targeting chimera (PROTAC) therapies degrading SWI/SNF ATPases offer a novel approach to interfere with androgen receptor (AR) signaling in AR-dependent castration-resistant prostate cancer (CRPC-AR). To explore the utility of SWI/SNF therapy beyond AR-sensitive CRPC, we investigated SWI/SNF-targeting agents in AR-negative CRPC. SWI/SNF targeting PROTAC treatment of cell lines and organoid models reduced the viability of not only CRPC-AR but also WNT-signaling dependent AR- negative CRPC (CRPC-WNT). The CRPC-WNT subgroup represents 11% of around 400,000 cases of CRPC worldwide who die yearly of CRPC. We discovered that SWI/SNF ATPase SMARCA4 depletion interfered with the master transcriptional regulator TCF7L2 (TCF4) in CRPC-WNT. Functionally, TCF7L2 maintains proliferation via the MAPK signaling axis in this subtype of CRPC. These data suggest a mechanistic rationale for interventions that perturb the DNA binding of the pro-proliferative TCF7L2 transcription factor (TF) and/or direct MAPK signaling inhibition in the CRPC-WNT subclass of advanced prostate cancer. Statement of significanceAndrogen receptor (AR)-negative prostate cancer (PCa) remains a clinical challenge due to the lack of targeted therapeutic options. Here, we identified a lineage-defining molecular axis in a subtype of AR- negative PCa, accounting for around 10% of castration-resistant PCa (CRPC) that can be interfered with by SWI/SNF-targeting agents.

cancer biology↗

bioRxiv2023

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

Transcription factors (TFs) and transcriptional coregulators represent an emerging class of therapeutic targets in oncology. Gene regulatory networks (GRNs) can be used to evaluate pharmacological agents targeting these factors and to identify drivers of disease and drug resistance. However, GRN methods that rely solely on gene expression often fail to account for post-transcriptional modulation of TF function. We present Epiregulon, a method that constructs GRNs from single-cell ATAC-seq and RNA-seq data for accurate prediction of TF activity. This is achieved by considering the co-occurrence of TF expression and chromatin accessibility at TF binding sites in each cell. We leverage ChIP-seq data to extend inference to transcriptional coregulators lacking defined motifs or TF harboring neomorphic mutations. Epiregulon accurately predicted the effects of AR inhibition across various drug modalities including an AR antagonist and an AR degrader, delineated the mechanisms of a SMARCA4 degrader by identifying context-dependent interaction partners and prioritized known and novel drivers of lineage reprogramming and tumorigenesis. By mapping gene regulation across various cellular contexts, Epiregulon can accelerate the discovery of therapeutics targeting transcriptional regulators.

bioinformatics↗