Investigating epigenetic diversity in cancer evolution
Dr C Swanton
Dr P Van Loo
Tuesday, November 12, 2019
Funded PhD Project (Students Worldwide)
The Van Loo and Swanton labs developed approaches to unravel the evolutionary history and subclonal architecture of cancer from DNA profiling data (1,2,3) and have applied them to a large-scale longitudinal cancer evolution programme, TRACERx, leading to key insights into tumour evolution, unravelling the mechanisms and impact of cancer cell diversity upon clinical outcome, metastases and immune evasion (3,4,5).
An emerging mechanism of cancer selection and adaptation derives from DNA methylation diversity across the genome resulting in diverse transcriptional changes from cell to cell. Recently, the Swanton-Van Loo collaboration has resulted in the development of computational approaches to interpret DNA methylation data, including deconvolution approaches that allow study of the tumour methylome untarnished by non-tumour cells (unpublished work). Reduced Representation Bisulfite Sequencing (RRBS) technologies are being applied to the TRACERx cancer dataset to investigate how epigenetic heterogeneity impacts cancer transcriptional changes across emerging subclones in the same tumour (5). This now provides a unique opportunity to understand how epigenomic changes influence the transcriptome, immune system and the evolution of tumours.
This multidisciplinary PhD project will:
Leverage current TRACERx RRBS analysis pipelines to (i) investigate the prevalence of epigenetic diversity, (ii) its associations with DNA-level and transcriptional diversity, and (iii) its impact on cancer subclonal selection and immune evasion within the tumour microenvironment.
Develop approaches to profile a tumour’s evolutionary history from RRBS data, apply them to TRACERx and other data, and integrate the results with evolutionary analyses from exome and genome sequencing data.
The project above is one example of a research project available as a collaboration between the Van Loo and Swanton groups – the precise project will be decided in consultation with the supervisors.
This position is suitable for a quantitative scientist (e.g. statistician, physicist, mathematician, bioinformatician) with a strong interest in biology/biomedicine, or a biologist with an interest in and talent for computational analyses.
1. S. Nik-Zainal et al., The life history of 21 breast cancers. Cell 149, 994-1007 (2012).
2. M. Gerstung et al. The evolutionary history of 2,658 cancers. bioRxiv preprint, doi: https://www.biorxiv.org/content/10.1101/161562v3 (2018).
3. M. Jamal-Hanjani et al., Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med 376, 2109-2121 (2017).
4. N. McGranahan et al., Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. Cell 171, 1259-1271 e1211 (2017).
5. R. Rosenthal et al., Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479-485 (2019).