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Prostate cancer is the most common cancer among men in the UK. It is diagnosed by histological analysis to identify cancer cells, and assigned a grade to reflect indicate the severity of disease. However, this grading is imperfect, often leading to under- or over-treatment. Intra-tumour heterogeneity which arises due to clonal expansion and selection (termed as cancer evolution) further complicates histological assessment. Adding genomic and transcriptomic information to the histological analysis can improve the accuracy of disease classification, as specific mutations are known to modify disease outcome. Our previous work in this area identified the genomic correlates of histological change (Rao et al., BioRxiv 2023Rao et al., Int J Mol Sci 2023Rao et al., J Mol Diagn. 2020). This DPhil project will build upon our previous work by combining genomics and transcriptomics with image analysis.  

We will apply deep learning-based image analysis tools to identify regions of histological transformation. We will identify single nucleotide variants (SNVs) and copy number alterations (CNAs) associated with this histological change using a novel spatial genomic analysis technique that we developed. Combining this with NanoString GeoMx Digital Spatial Profiling to also identify transcriptomic changes, we aim to build a comprehensive multi-omic profile of molecular alterations associated with specific histological changes in prostate cancer. Using a multi-scale approach will also enable us to perform a phylogenetic analysis to identify the different cancer clones in the different histological regions. The spatial molecular data thus obtained can also be mapped back to image data to deconvolute bulk sequencing data, e.g. from the TCGA dataset. This project will be supervised by Prof. Claire Edwards, Dr. Srinivasa Rao and Prof. Clare Verrill.

We are a truly multi-disciplinary team consisting of experts in molecular biology and bioinformatics (Dr. Rao), prostate cancer biology (Prof. Edwards), histopathology (Prof. Verrill) working at the cutting edge of prostate cancer research. We invite applications from enthusiastic students interested in using a multi-disciplinary approach to answer questions in prostate cancer biology. Candidates are expected to have a keen interest in, and the aptitude to learn, techniques spanning molecular biology (short- and long-read sequencing library preparation, laser capture microdissection, immunohistochemistry, digital spatial profiling), bioinformatics (genomic/transcriptomic data analysis using R, Python and Unix shell, workflow management with Snakemake, data integration and visualization). There are opportunities for the successful candidate to learn these techniques from the supervisors and collaborators who have supervised several DPhil, masters and undergraduate projects to successful completion. In addition to this, DPhil students have access to numerous courses run by the University or the Division (including courses led by Dr. Rao on R and Snakemake). 

Please contact Dr. Rao ( for informal inquiries.