Understanding the evolution of extrachromosomal DNA through treatmnent and metastastic progression

Primary supervisor: Charles Swanton, Francis Crick Institute

Secondary supervisor: Benjamin Werner, Queen Mary University of London

Project

Understanding cancer metastases is fundamental to improving clinical outcomes. TRACERx has revealed evidence that the future metastatic subclone(s) are subject to selection in the primary tumour (Frankel et al. 2023; Al-Bakir et al. 2023) and transcriptomic plasticity drives evolutionary fitness of disseminating subclones (Martinez-Ruiz et al. 2023). Full resolution of the metastatic process and an understanding of how cancer evolves within the patient and causes death have been hampered by limited tumour sampling of metastatic and primary tumour sites (Al Bakir et al. 2023).

TRACERx EVO, a recently funded CRUK longitudinal lung cancer evolution program, builds on the learnings from TRACERx in order to address these challenges. TRACERx EVO is recruiting patients with early and late-stage NSCLC in a longitudinal program integrated with the PEACE national autopsy program to enable deeper and broader analysis of each metastatic site at death. Cross sectional CT imaging analyses will be integrated with whole genome sequencing and transcriptomic analysis of primary and metastatic sites combined with tumour microenvironment analysis using state-of-the-art mass cytometry imaging technologies.

Extrachromosomal DNA (ecDNA) is emerging as a major contributor to cancer drug resistance and poor outcome that is present in many cancer types including NSCLC. Using whole genome sequencing data from 15,226 cancer patients from 39 tumour types in the Genomics England (GEL) cohort, we observed focal amplifications driven by ecDNA in 17.1% of the tumour samples studied. We observed that ecDNA is associated with metastasis and shorter survival and that certain mutational signatures such as that associated with polymerase epsilon were negatively associated with the presence of ecDNA in tumours. To build on these learnings in TRACERx Evo, the specific aims of the project are:

1. To characterise ecDNA evolution in response to chemotherapy and immunotherapy in NSCLC
2. To validate existing models of ecDNA driven selection with patient derived data in TRACERx
3. To understand whether ecDNA is carried in the metastatic subclone

Candidate background

This post seeks an enthusiastic clinical research training fellow, keen to work within a multi-disciplinary team of biologists, bioinformaticians and clinicians, with interests in biostatistics, computational biology or mathematics, who is keen to learn computational biological skills to decipher lung cancer evolution from diagnosis through to death. This project would also suit candidates with an interest in epigenetics and immunology. It is desirable but not essential for the candidate to have basic preliminary knowledge of R or Python and experience in data analysis.

References

1. Al Bakir et al The evolution of non-small cell lung cancer metastases in TRACERx. Nature. 2023 Apr;616(7957):534-542. doi: 10.1038/s41586-023-05729-x. Epub 2023 Apr 12. PMID: 37046095
2. Martinez-Ruiz C et al Genomic-transcriptomic evolution in lung cancer and metastasis. Nature. 2023 Apr;616(7957):543-552. doi: 10.1038/s41586-023-05706-4. Epub 2023 Apr 12. PMID: 37046093
3. Frankell et al The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature. 2023 Apr;616(7957):525-533. doi:
4. Mcgranahan et al Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. Cell. 2017 Nov 30;171(6):1259-1271.e11. doi: 10.1016/j.cell.2017.10.001. Epub 2017 Oct 26. PMID: 29107330
5. Rosenthal et al Neoantigen-directed immune escape in lung cancer evolution. Nature. 2019 Mar;567(7749):479-485. doi: 10.1038/s41586-019-1032-7. Epub 2019 Mar 20. PMID: 30894752