Defining genomic drivers and novel structural variants in Triple Negative and hereditary breast cancers
Primary supervisor: Nnenna Kanu, UCL
Secondary supervisor: Elinor Sawyer, King’s College London
Project
Triple Negative Breast Cancer (TNBC) is an aggressive and highly heterogeneous subtype of breast cancer, often affecting younger women and associated with poor clinical outcomes [1]. Despite its recognised importance, a comprehensive understanding of tumour heterogeneity in breast cancer, particularly how it drives tumour evolution, relapse, and metastasis, remains an unmet clinical and scientific challenge. Long-read whole genome sequencing is transforming our ability to study the cancer genome [2]. Unlike traditional short-read technologies, long-read platforms such as Oxford Nanopore Technologies (ONT) offer improved resolution of complex structural variants and repetitive regions, and enable the phasing of variants across long genomic distances. Importantly, ONT also provides direct detection of DNA methylation, allowing the simultaneous analysis of genetic and epigenetic features from a single dataset.
While germline mutations in BRCA1 and BRCA2 are well-known contributors to hereditary breast cancer, many families with a strong history of breast cancer do not carry known pathogenic variants in these or other established susceptibility genes [3]. Using long-read whole genome sequencing data from 500 individuals from 50 families with a predisposition to breast cancer, the student will explore the landscape of germline structural variants and epigenetic alterations that may contribute to inherited risk. The project will involve applying existing bioinformatics tools as well as developing new computational methods tailored to the challenges of long-read data. A key goal is to identify novel, clinically relevant variants that have been missed by previous short-read analyses.
Extending the approaches established in the landmark lung TRACERx study, we also aim to investigate tumour evolution in TNBC using data from the Breast TRACERx cohort. We will leverage multi-regional and longitudinal sequencing data from tumour samples, enabling the analysis of how tumours respond to therapy, acquire resistance, and progress. The student will use computational approaches to reconstruct tumour phylogenies, identify subclonal architecture, and correlate mutational patterns with clinical outcomes.
This project offers the opportunity to contribute to both fundamental cancer research and translational applications. The student will gain training in long-read sequencing, variant detection, epigenomic analysis, and cancer genomics, while working with a multidisciplinary team including researchers from genomics, clinical oncology, and computational biology backgrounds.
Candidate background
This project would suit candidates with a background in computational biology, bioinformatics, genomics, or a related discipline. Strong programming skills (e.g. Python, R, or Bash) and familiarity with high-throughput sequencing data are essential. Prior experience with cancer genomics would be advantageous, but is not required. A keen interest in cancer biology, data integration, and method development is highly desirable. We welcome applicants who are enthusiastic about applying computational approaches to impactful biological and clinical questions.
Potential Research Placements
- Elinor Sawer, Comprehensive Cancer Centre, King’s College London
- Charles Swanton, The Francis Crick Institute
- Simone Zaccaria, Cancer Institute, UCL
References
- Aguilar-Mahecha A, Alirezaie N, Lafleur J, Bareke E, Przybytkowski E, Lan C, et al. The mutational spectrum of pre- and post-neoadjuvant chemotherapy triple-negative breast cancers. Genes (Basel). 2023 Dec 23;15(1):27.doi: 10.3390/genes15010027.
- Gustafson JA, Gibson SB, Damaraju N, Zalusky MPG, Hoekzema K, Twesigomwe D, et al. High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation. Genome Res. 2024 Nov 20;34(11):2061–73. doi:10.1101/gr.279273.124. PMID: 39358015.
- Couch FJ, Shimelis H, Hu C, Hart SN, Polley EC, Na J, et al. Associations between cancer predisposition testing panel genes and breast cancer. JAMA Oncol. 2017 Sep 1;3(9):1190–6. doi:10.1001/jamaoncol.2017.0424.