Unravelling complex somatic genomic variations: advancing early diagnostics and precision medicine

About the Project

Complex somatic structural variations (SVs) in genomic regions such as centromeres and telomeres play a crucial role in driving many genetic disorders. For example, large SVs in centromeric regions contribute to chromosomal instability (CIN) and therapy resistance in cancers like glioblastoma and lung cancer. Despite their importance, these repetitive regions remain insufficiently explored due to the limitations of current sequencing technologies, restricting our understanding of key somatic mutations, mobile genetic elements (MGEs), and critical biomarkers for early diagnosis. Traditional methods struggle to accurately analyze complex structural variations (SVs) in centromeres, telomeres, and extrachromosomal DNA (ecDNA) due to the repetitive and intricate nature of these genomic regions. This project will bridge these gaps by employing graph-based models and integrating long-read sequencing with Hi-C data from bulk and/or single-cell datasets at a personalized, chromosome-specific base level. Our approach will overcome the limitations of traditional linear genome analysis, enabling precise mapping of SVs and chromosome-level interactions, with the scalability to analyze thousands of genomes efficiently. By focusing on complex somatic SVs including chromothripsis, chromoplexy, and ecDNA, we aim to uncover a comprehensive somatic SV landscape essential for understanding the mechanisms driving cancer progression.

Specific Aims

• Aim 1: Develop a graph-based method for accurately analyzing repetitive genomic regions to advance early disease diagnostics
  • Create a graph-based method using long-read sequencing and Hi-C data to detect somatic variations in complex, repetitive regions like centromeres and telomeres.
  • Identify and characterize classes of inter- and intra-chromosomal structural variations, including chromothripsis and chromoplexy, and assess their functional consequences.
• Aim 2: Investigate the role of extrachromosomal DNA (ecDNA) in disease progression
  • Develop a toolkit feature that captures the circular nature of ecDNA to explore its role in disease progression and gene amplification.
• Aim 3: Comprehensively unravel the processes of clonal evolution in personalized cancer
  • Generate and validate a personalized set of structural variations in cancer cell lines, using the tools developed in Aims 1 and 2, across both commercially available and patient-derived cell lines.

By using a graph-based approach, we can precisely map centromeric disruptions, such as alpha-satellite DNA instability and EGFR amplification, in brain, lung, and oesophageal cancers, providing insights into clonal evolution and resistance mechanisms. This method overcomes the limitations of linear reference genomes, enabling personalized treatment strategies by accurately tracking structural variations and predicting tumor responses to therapies, ultimately improving patient outcomes.

Outcomes

This project will deliver a comprehensive map of structural variations (SVs) in centromeric and telomeric regions, advancing our understanding of how these variations drive cancer progression and rare diseases. By developing graph-based methods, we will enable the detection of previously hidden SVs, supporting early diagnostics and personalized treatments tailored to each patient’s unique genetic profile. The project’s insights into chromosomal instability, gene amplification, immune evasion, and the regulatory roles of mobile genetic elements will deepen our knowledge of fundamental disease mechanisms. The project will illuminate the broader implications of SVs on genome evolution, gene regulation, and disease mechanisms. Through collaborations with healthcare partners, we aim to integrate these innovations into clinical pipelines, improving outcomes for conditions with significant unmet needs.

The PGR will receive comprehensive support and training designed to foster expertise in genomics and disease biology. Complementing the set of technical skills, the PGR will develop essential professional skills in project management, academic writing, and data visualization, with access to workshops, writing retreats, and industry-focused seminars. This holistic training approach will equip the PGR with the knowledge and skills needed to make significant contributions to clinical applications. Students are expected to be highly motivated to work on challenging research questions in an international team and collaborative environment.

Our investigation will also focus on somatic instability and clonal evolution in nervous tissue by analysing repeat expansions in genes like HTT (Huntington’s Disease) and DMPK (Myotonic Dystrophy).

Eligibility

Applicants must have obtained or be about to obtain a minimum Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in a relevant discipline.

How to Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website. Informal enquiries may be made directly to the primary supervisor. On the online application form please select PhD Bioinformatics.

Funding Notes

Applications are invited from self-funded students.