Key information
Research topics
Computational & Systems Biology Ecology, Evolution & Ethology Genetics & Genomics Immunology Tumour Biology
A 2023 Crick PhD project with Samra Turajlic.
Project background and description
There are two potential projects that the successful candidate could undertake, these are outlined below.
Evolutionary principles are increasingly applied to help us understand cancer initiation and progression as evidenced by our work to date [1-4]. In the context of renal cancer, we observe that loss of the short arm of chromosome 3 (3p loss) is the very first event which we estimate happens around adolescence in the proximal tubules of all individuals and precedes malignant transformation by many decades. Normal cells should not tolerate aneuploidy, yet 100s of such cells must survive in the kidney for the second event (VHL mutation) to ever become fixed, leading to tumours. When we sequence tumours we are unable to identify this ancestral population as it had been lost, akin to fossil records. Yet, the key to understanding and preventing kidney cancer is deciphering how 3p loss is tolerated, why specifically in the kidney, and the conditions under the second hit leads to clonal fixation. We will study this through a combination of patient tissue molecular and spatially resolved multi-omic profiling (normal and cancerous tissues both in the context of sporadic kidney cancer and inherited VHL disease) as well as in vitro studies of induction of 3p loss in preclinical models (derived form surgical resections or IPSC lines into organoids) and test the range of conditions under which these cells survive.
The second potential project relates to the other end of the spectrum, cancer metastases and death. In renal cancer, we observe that the mode of evolution in the primary tumour can determine the growth of the primary tumour and the patterns of metastatic progression. Renal cancer in particular exhibits a wide range of metastatic phenotypes - from very latent metastases with predilection for endocrine tissues (hinting at dormancy); oligometastases where metastases are confined to a single organ; and widespread/rapid metastases. To build on this we will decipher the evolutionary dynamics of renal cancer metastases, characterizing the sources of selection that lead to emergence of metastasis-competent clones at the site of the primary tumour; understand the patterns and timing of metastatic spread; identify novel/common vulnerabilities within the metastatic process, that may inform new strategies for therapeutic targeting; and understand the mechanisms of resistance to targeted and immune-oncology agents. This is very pertinent in the context of renal cancer because the therapies used to treat the disease target the tumour microenvironment (angiogenesis or immune cells). Through established research studies, TRACERx Renal and a post-mortem (PEACE) Study, there is a unique opportunity to sample a whole range of metastatic sites. The TRACERx Renal study has complete recruitment and we have already performed 20 renal cancer post-mortem with both studies contributing 100s of samples that undergo whole exome, genome and RNA sequencing, generating the largest dataset of this type worldwide. This is an opportunity to work with this dataset to provide unprecedented insights into patterns of renal cancer spread and the variation in evolutionary patterns between cases. There is also an opportunity to contribute to mathematical modelling to predict cancer evolution [5, 6].
Understanding evolutionary dynamics in renal cancer also has the potential to inform design of future clinical trials with regards to the timing of therapy. The current paradigm rests upon an assumption of linear progression and will be challenged in this project.
The candidate will be based in a multi-disciplinary team of cancer biologists and translational research clinicians concerned with both basic evolutionary principles and application of evolutionary rules in the clinic. The expertise represented in the lab includes pathology, machine learning, bioinfomatics, spatial biology, genome engineering, single cell sequencing. We collaborate nationally and internationally and there will be ample opportunities for training at the Crick and beyond.
Candidate background
The particular project will be decided on discussion with the candidate and understanding their aptitude, passion and aspirations. A dry lab project would suit a candidate with a background in mathematics, physics, statistics, bioinformatics, evolutionary biology who has an interest in cancer evolution, cancer biology and translational research. Prior experience of computational methods especially in cancer genomics and cancer evolution is useful, but ample opportunities for training will be provided to those without such experience. A wet lab project would suite a candidate with a background in cancer biology, biochemistry, gene function, spatial biology, with an interest in normal tissue mutagenesis, chromosomal instability and tumour microenvironment.
References
1. Turajlic, S. and Swanton, C. (2016)
Metastasis as an evolutionary process.
Science 352: 169-175. PubMed abstract
2. Turajlic, S., Larkin, J. and Swanton, C. (2015)
SnapShot: Renal cell carcinoma.
Cell 163: 1556-1556.e1551. PubMed abstract
3. Turajlic, S., Xu, H., Litchfield, K., Rowan, A., Chambers, T., Lopez, J.I., . . . TRACERx Renal Consortium (2018)
Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal.
Cell 173: 581-594.e512. PubMed abstract
4. Turajlic, S., Xu, H., Litchfield, K., Rowan, A., Horswell, S., Chambers, T., . . . TRACERx Renal Consortium (2018)
Deterministic evolutionary trajectories influence primary tumor growth: TRACERx Renal.
Cell 173: 595-610.e511. PubMed abstract
5. Zhao, Y., Fu, X., Lopez, J.I., Rowan, A., Au, L., Fendler, A., . . . Litchfield, K. (2021)
Selection of metastasis competent subclones in the tumour interior.
Nat Ecol Evol 5: 1033-1045. PubMed abstract
6. Fu, X., Zhao, Y., Lopez, J.I., Rowan, A., Au, L., Fendler, A., . . . Bates, P.A. (2022)
Spatial patterns of tumour growth impact clonal diversification in a computational model and the TRACERx Renal study.
Nat Ecol Evol 6: 88-102. PubMed abstract
