Matthew Eldridge

The estrogen receptor-α (ER) drives 75% of breast cancers. On activation, the ER recruits and assembles a 1–2 MDa transcriptionally active complex. These complexes can modulate tumour growth, and understanding the roles of individual proteins within these complexes can help identify new therapeutic targets. Here, we present the discovery of ER and ZMIZ1 within the same multi-protein assembly by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating a significant decrease in the proliferation of ER-positive cancer cell lines. To establish a role for the ER-ZMIZ1 interaction, we measured the transcriptional changes in the estrogen response post-ZMIZ1 knockdown using an RNA-seq time-course over 24 h. Gene set enrichment analysis of the ZMIZ1-knockdown data identified a specific delay in the response of estradiol-induced cell cycle genes. Integration of ENCODE data with our RNA-seq results identified that ER and ZMIZ1 both bind the promoter of E2F2. We therefore propose that ER and ZMIZ1 interact to enable the efficient estrogenic response at subset of cell cycle genes via a novel ZMIZ1–ER–E2F2 signalling axis. Finally, we show that high ZMIZ1 expression is predictive of worse patient outcome, ER and ZMIZ1 are co-expressed in breast cancer patients in TCGA and METABRIC, and the proteins are co-localised within the nuclei of tumour cell in patient biopsies. In conclusion, we establish that ZMIZ1 is a regulator of the estrogenic cell cycle response and provide evidence of the biological importance of the ER–ZMIZ1 interaction in ER-positive patient tumours, supporting potential clinical relevance.

Analysis of circulating tumor DNA (ctDNA) to monitor cancer dynamics and detect minimal residual disease has been an area of increasing interest. Multiple methods have been proposed but few studies have compared the performance of different approaches. Here, we compare detection of ctDNA in serial plasma samples from patients with breast cancer using different tumor‐informed and tumor‐naïve assays designed to detect structural variants (SVs), single nucleotide variants (SNVs), and/or somatic copy‐number aberrations, by multiplex PCR, hybrid capture, and different depths of whole‐genome sequencing. Our results demonstrate that the ctDNA dynamics and allele fractions (AFs) were highly concordant when analyzing the same patient samples using different assays. Tumor‐informed assays showed the highest sensitivity for detection of ctDNA at low concentrations. Hybrid capture sequencing targeting between 1,347 and 7,491 tumor‐identified mutations at high depth was the most sensitive assay, detecting ctDNA down to an AF of 0.00024% (2.4 parts per million, ppm). Multiplex PCR targeting 21–47 tumor‐identified SVs per patient detected ctDNA down to 0.00047% AF (4.7 ppm) and has potential as a clinical assay.