The post is funded through a Cancer Research UK Biology to Prevention project grant. The key questions we aim to answer are:
What structural features define high-risk breast tissue, and can these be identified by mammography?
Can we predict responders and non-responders prior to prevention treatment?
Can an understanding of the biology of risk identify novel prevention targets?
The project will employ two parallel strategies. One will be to undertake high resolution analysis of breast tissue composition and its response to tamoxifen. The second will be to use machine learning analysis of serial mammograms collected in large screening programs. This specific post will be focussed on the first approach.

• Identify the biological features of resistance to preventive tamoxifen in breast tissue biopsies.
• Define the mechanistic effect of tamoxifen to identify novel prevention targets using ex vivo explant cultures.
  The approach taken will be to determine changes in tissue composition and gene expression in serial biopsies taken from women undergoing preventive Tamoxifen, employing spatial RNA sequencing, multiplexed imaging and proteomics. We will also use a tissue explant model to examine the effects of tamoxifen using functional genomics, such ATAC-seq and proteomic analysis of transcription factor complexes by qPLEX-RIME.