Tracking cancer evolution using ctDNA from early to late stage disease in lung cancer
Primary supervisor: Mariam Jamal-Hanjani, UCL
Secondary supervisor: Charles Swanton, Francis Crick Institute/ UCL
Tertiary supervisor: Amanda Fitzpatrick, King’s College London
Project
Intratumour genomic heterogeneity is associated with tumour aggressiveness, therapeutic resistance and poor outcomes (1). Deciphering the longitudinal dynamics of tumour composition can lead to insights into tumour evolution, response to therapy and metastatic recurrence (2). However, tissue sampling is restricted by challenges in sample acquisition, especially in patients who may have disease at inaccessible anatomical sites or co-morbidities impacting the ability of patients to undergo invasive sampling. Longitudinal blood sampling for circulating tumour DNA (ctDNA) offers a non-invasive approach to detect the presence of cancer DNA in the blood and can be used in conjunction with radiological imaging to track the disease course from diagnosis to relapse, and in response to treatment.
This project leverages the resource of blood and tissue established by the national TRACERx lung cancer evolution programme (3) and PEACE research autopsy study, both led by UCL. Over 820 patients have been recruited into TRACERx in which over 500 primary tumours at diagnosis have been subjected to multi-region deep whole-exome sequencing. Over 200 research autopsies have been performed in PEACE to date. Genomic profiling of matched primary and metastatic tumours across both studies has enabled a detailed analysis of tumour evolution from diagnosis to death. This project aims to use ctDNA to track the disease course from diagnosis to death using personalised amplicon panels targeting tumour-specific alterations in ctDNA to determine when metastatic clones arise and the dynamics of such clones over time, in relation to cancer treatment and metastatic progression. Longitudinal radiological imaging performed as standard of care will be integrated with this analysis to specifically track ctDNA clonal dynamics in relation to the development and growth of metastases at specific anatomical sites, which subsequently may have been sampled at autopsy. Finally, this project aims to investigate the utility of ctDNA in tracking the immune landscape and neoantigen repertoire and how this evolves from early to late stage disease, and in response to immunotherapies. This project will benefit from many orthogonal data sets across the studies, combing tumour genomics, transcriptomics, epigenomics and immune profiling, as well metabolomic and immune cell phenotyping studies in the blood. Outputs from this project have the potential to inform the utility of ctDNA in tracking tumour dynamics, predicting disease relapse and the emergence of drug resistance.
Candidate background
The successful candidate should have a background in either life sciences related fields or bioinformatics. It is desirable but not essential for the candidate to have basic preliminary knowledge of R or Python and experience in data analysis. The successful candidate should have an interest and ability to develop understanding of complex problems and apply in-depth knowledge to address them. This project involves collaboration across multiple disciplines, including clinical medicine, laboratory science, statistical analysis, computational biology and bioinformatics.
Potential Research Placements
- Amanda Fitzpatrick, Innovation Hub, King’s College London
- Nnenna Kanu, UCL Cancer Institute
- Nicholas McGranahan, UCL Cancer Institute
References
- Bailey et al. Tracking Cancer Evolution through the Disease Course. Cancer Discov. 2021 Apr;11(4):916-932. doi: 10.1158/2159-8290.CD-20-1559.
- Abbosh C et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature. Nature Publishing Group; 2017 Apr 26;545(7655):446-51. doi: 10.1038/nature22364.
- Jamal-Hanjani M et al. Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med. Massachusetts Medical Society; 2017 Jun 1;376(22):2109-21. doi: 10.1056/NEJMoa1616288.