Insights into Transcription/Translation in Prostate Cancer through analyses of novel data sets

Approximately one in eight men will be diagnosed with prostate cancer (PCa) in his life, while a substantially higher proportion of men would show evidence of PCa under histopathological examination. For many, the disease will cause no symptoms and the burden of treatment on such cases would be entirely disproportionate. There is therefore a long-standing interest in methods to sub-classify PCa cases, with RNA expression being one of the most commonly used tools in the literature.

RNA expression profiles of prostate cancer have been used to develop diagnostic and prognostic signatures, as well as to make inference about characteristics such as cell-type mixture levels, BMI, glycosylation activity etc. However many of these signatures are empirically derived, and their individual components are difficult to interpret – all the more so since messenger RNA (mRNA) expression and protein expression profiles are typically not highly correlated.

As well as mRNA and protein expression, we have several other expression assays available to us. Small RNA-seq allows us to profile several classes of non-coding RNA that contribute to the regulation of transcription and translation, SHAPE-seq and Ribo-seq are less-commonly used technologies that inform about translation of mRNA to protein (reporting on RNA conformation and Ribosomal occupancy respectively).

Several large-scale sequencing studies have profiled mRNA and small RNA expression in prostate cancer including The Cancer Genome Atlas (TCGA) and several International Cancer Genome Consortium (ICGC) projects. This project is affiliated to the UK ICGC Prostate project headed by Professor Ros Eeles at the Institute of Cancer Research and additionally involving PIs in Cambridge, Norwich, Oxford, St Andrews and Tampere. The diversity of the broader research team including clinicians, pathologists, computer scientists and data scientists is a particular strength of the larger project.

The PhD Project
This project is a mixture of methodology development and data analysis, and will focus on interpreting and integrating these different types of expression data. This will include a first analyses of some data-types in prostate cancer, while contributing to the primary analysis of small RNA-seq data for the ICGC project, and development and application of methods for integrating all of these expression data-types to gain insight into the biology of the disease. Clinical and pathological data, as well as DNA sequencing, are all also available for these PCa cases.

As with much work in cancer ‘big data’ projects, there is a desire to use what we learn about the biology of the disease to inform future patient care. In particular it is an ambition to identify sub-groups with common features that either have value prognostically, or are suggestive of tailored treatment options, or can be used to monitor the disease. This work will also enable investigation of existing classifiers and signatures in prostate cancer, and through increased understanding of the biology underlying those tools, will give greater guidance on how they should be interpreted and used.

The Person
Applicants should have either a first degree or, ideally, a Masters degree containing a large component of statistics, computational biology or similar. Experience of coding and scripting in R is highly desirable. The ideal candidate will have either an existing interest in genomics or an enthusiasm for learning about molecular biology. Some experience of working with genetic/genomic data would be desirable but not essential.