Melanoma evolution and its impact on therapy responses

Deadline for applications has passed.

Key information

Applications closed
07 February 2023, 11:59 GMT
Hours per week
36 (full time)
Application guidance
Posted 22 December 2022

Research topics

Computational & Systems Biology Gene Expression Genetics & Genomics Tumour Biology
Background texture taken from the lab imagery.

This is a summer student position supervised by Irene Lobon from Samra Turajlic's lab. 

Introduction to the Science

We are a multi-disciplinary group of cancer biologists, computational biologists and clinician scientists studying melanoma and kidney cancer. The focus of our lab is cancer evolution, from its development to how it adapts and becomes resistant to therapy. We believe that understanding the forces and mechanisms behind tumour progression and survival will help us improve the therapeutic strategies.


About the Project

The treatment of advanced melanoma was revolutionised in the past decade with the use of immunotherapy. Sadly, still over half of patients die from the disease, creating a need to understand how melanoma cells adapt. We carry out bulk and single cell sequencing of tumour samples from patients, then analyse the data to help us (1) understand how cancer cells change under the evolutionary pressure of therapy and (2) identify common features among resistant tumours. Not all metastatic tumours in a patient behave in the same way, so we evaluate how similar they are and reconstruct their evolutionary history to infer when tumour cells migrated to other organs. We also integrate datasets generated by other research groups to ensure our findings are robust. A summer student could help us with analysing raw data, generating graphics from processed data, implementing tools, and/or organising data and reviewing the relevant literature.


About You

This project would suit a candidate interested in bioinformatics. Either someone studying biological sciences who wants to learn about computational methods or someone studying computational science who wants to learn about how to apply their knowledge in the biomedical field.



1.         Rogiers, A., Lobon, I., Spain, L. and Turajlic, S. (2022)

            The genetic evolution of metastasis.

            Cancer Research 82: 1849-1857. PubMed abstract

2.         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