In silico identification of proliferation drivers in the pan cancer genome using systems biology approaches.

Since the sequencing of the human genome new approaches for studying disease systems at the genomic, epigenetic, proteomic and metabolomics levels are being continually developed. One of the challenges with the analysis of such data is the volume, resolution and complexity of the data generated; plus the quality of the data. These issues from a bioinformatics perspective are often typified by the criticism that different data sets do not yield consistent results. This would indicate that often such individual data sets have high levels of noise and thus do not have sufficient cases to achieve a sufficient statistical power. Thus analysis of such data requires careful consideration, paying attention to the non-linearity of biological systems, the interaction of molecular entities in pathways, the fluidity of biological systems and the need for determination of consistent entities across multiple data sets.

We have developed cutting edge systems biology and bioinformatics approaches, based on computational intelligence, which identify robust biomarkers associated with clinical features which are concordant across multiple data sets. Furthermore, we have developed approaches which study the interactions between key features in the context of a given problem. These approaches in effect determine the level of influence of a set of driver markers in a given biological system. This approach allows us to determine the molecular drivers of a system which result in a given phenotype.

This project builds upon successful previous work of doctoral students in the van Geest Cancer Research Centre who have developed and applied the methodology to identify markers of proliferation in breast cancer. (Abdel Fatah et al., 2016 Lancet Oncology 17:1004-18). The project uses these methods to identify and investigate robust biomarkers of cancer processes, such as proliferation, across publicly available cancer transcriptomic data sets. After full characterisation these will be validated in conjunction with clinical partners.

Specific qualifications/subject areas required of the applicants for this project

Applicants should hold, or be expected to hold, an UK Master’s degree (or UK equivalent according to NARIC) with a minimum of a commendation, and/or a UK 1stClass / 2.1 Bachelor’s Honour’s Degree (or UK equivalent according to NARIC) in Biology, Biochemistry, Biomedical Sciences or Bioinformatics.