Cancer evolution, drift and microenvironment selection

TRACERx is a 10-year longitudinal study of lung cancer evolution from diagnosis to cure or cancer relapse and death implementing DNA and RNA multi-region deep sequencing data with clinical outcome, imaging and immune microenvironmental analysis using imaging mass cytometry in over 700 patients. The study has revealed multiple mechanisms generating cancer cell diversity including APOBEC mutagenesis [1], loss of cancer immune recognition [2, 3], cancer cytotoxics [4], genome doubling events [1] and DNA replication stress [5].

However, inferring selection or neutral evolution from cancer deep sequencing data and how these change over time in a dynamic environment has remained problematic. Moreover, whether selection pressures dictate metastatic potential and its timing remain unclear. Ultimately a more detailed understanding of cancer evolutionary biology may help differentiate “born-to-be-bad” tumours that disseminate widely upon tumour initiation from tumours which progress more indolently or never metastasise. A deeper understanding of these questions will have a fundamental impact on our understanding of disease biology, drug resistance and treatment failure.

Candidate background
The candidate should have a background in one or more of the following: mathematics, statistics , bioinformatics or evolutionary biology. No prior experience in cancer biology is required.

TRACERx represents the most complete dataset in which to address these challenges. This project will work with TRACERx biological, genomics and clinical data in collaboration with the UCL Cancer Institute (McGranahan) to develop state-of-the-art tools to model cancer selection and neutrality and infer the likely tumour micro-environmental influences upon cancer evolutionary dynamics from primary through to metastatic disease across hundreds of patients.