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Biology subjects

Gehrung, M.

Publications and source records attributed to Gehrung, M..

3 recordsLinked to original sources

ZMIZ1 - A novel Estrogen Receptor co-activator that enhances the growth of ER+ breast cancer

The Estrogen Receptor (ER) drives 75% of breast cancers. On activation, the ER recruits co-factors to form a transcriptionally active complex. These co-factors can modulate tumour growth, and understanding their roles can help to identify new therapeutic targets. Here, we present the discovery of an ER-ZMIZ1 interaction by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating that targeting ZMIZ1 results in the reduction of ER transcriptional activity at estrogen response elements and 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 hours. GSEA 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 ER and ZMIZ1 binding at the promoter of E2F2. We therefore propose that ER and ZMIZ1 co-regulate an important subset of cell cycle genes via a novel ER-ZMIZ1-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, METABRIC, and the proteins are co-localised within the nuclei of tumours 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 ER+ patient tumours, supporting potential clinical relevance.

cancer biology↗

3D-printed moulds of renal tumours for image-guided tissue sampling in the clinical setting

PURPOSESpatial heterogeneity of tumours is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood, as it relies on the accurate co-registration of medical images and tissue biopsies. tumour moulds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumour heterogeneity data. METHODSWe have developed an open source computational framework to automatically produce patient-specific 3D-printed moulds that can be used in the clinical setting. Our approach achieves accurate co-registration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging and pathology workflows. RESULTSWe applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalised moulds for five patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.93 for tumour regions, and between 0.70 and 0.76 for healthy kidney. The framework required minimal manual intervention, producing the final mould design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes. CONCLUSIONOur work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumour heterogeneity on multiple spatial scales.

bioengineering↗

Co-Registration of Optoacoustic Tomography and Magnetic Resonance Imaging Data from Murine Tumour Models

As optoacoustic tomography emerges as a mainstream preclinical imaging modality, understanding the relationship between optoacoustic and other imaging biomarkers in the context of the underlying tissue biology becomes vitally important. For example, assessment of blood haemoglobin concentration and oxygenation can be achieved using OT, and also by several magnetic resonance imaging (MRI)-based techniques. To evaluate the relationship between these metrics and the relative performance of the two modalities in assessment of haemoglobin physiology, co-registration of their output imaging data is required. Unfortunately, this poses a significant challenge due to differences in the data acquisition geometries. Here, we present an integrated framework for registration of OT and MR image data in small animals. Our framework combines a novel MR animal holder, to improve animal positioning for deformable tissues, and a landmark-based software co-registration algorithm. We demonstrate that our protocol significantly improves registration of both body and tumour contours between these modalities.

bioengineering↗