Exploring the Spatial Landscape of the Estrogen Receptor Proximal Proteome With Antibody-Based Proximity Labeling.

ABSTRACT

Estrogen receptor α (ERα) drives the transcription of genes involved in breast cancer (BC) progression, relying on coregulatory protein recruitment for its transcriptional and biological activities. Mutation of ERα as well as aberrant recruitment of its regulatory proteins contribute to tumor adaptation and drug resistance. Therefore, understanding the dynamic changes in ERα protein interaction networks is crucial for elucidating drug resistance mechanisms in BC. Despite progress in studying ERα-associated proteins, capturing subcellular transient interactions remains challenging and, as a result, significant number of important interactions remain undiscovered. In this study, we employed biotinylation by antibody recognition (BAR), an innovative antibody-based proximity labeling (PL) approach, coupled with mass spectrometry to investigate the ERα proximal proteome and its changes associated with resistance to aromatase inhibition, a key therapy used in the treatment of ERα-positive BC. We show that BAR successfully detected most of the known ERα interactors and mainly identified nuclear proteins, using either an epitope tag or endogenous antibody to target ERα. We further describe the ERα proximal proteome rewiring associated with resistance applying BAR to a panel of isogenic cell lines modeling tumor adaptation in the clinic. Interestingly, we find that ERα associates with some of the canonical cofactors in resistant cells and several proximal proteome changes are due to increased expression of ERα. Resistant models also show decreased levels of estrogen-regulated genes. Sensitive and resistant cells harboring a mutation in the ERα (Y537C) revealed a similar proximal proteome. We provide an ERα proximal protein network covering several novel ERα-proximal partners. These include proteins involved in highly dynamic processes such as sumoylation and ubiquitination difficult to detect with traditional protein interaction approaches. Overall, we present BAR as an effective approach to investigate the ERα proximal proteome in a spatial context and demonstrate its application in different experimental conditions.