Computational methods for the detection of somatic structural variants in cancer genomes using long-read sequencing

Accurate detection of somatic structural variants (SVs) is critical for informing the diagnosis and treatment of human cancers. In this thesis, I present SAVANA, a computational method for the analysis of somatic SVs using long-read whole genome sequencing data from tumours and matched normal samples. SAVANA employs machine learning to distinguish true somatic SVs from germline events and noise. Additionally, I establish best practices for benchmarking SV detection using simulated and sequencing replicates to demonstrate SAVANA’s superior sensitivity, specificity, and speed compared to existing methods. I show that SAVANA performs robustly across a variety of clonality levels, genomic regions, SV types, and sizes. Using Illumina and Oxford Nanopore whole-genome sequencing data from 99 tumours and matched normal samples of patients, I show that SVs reported by SAVANA are highly concordant with those detected using short-read sequencing, including in regions of complex structural variation. I also highlight the enhanced ability of long-reads to identify SVs in repetitive regions where short-reads are unable to map with high confidence. In summary, this thesis introduces SAVANA as a novel computational method to identify somatic SVs in long-reads, establishes a robust framework for benchmarking SV detection, and demonstrates the method’s high consistency and enhanced sensitivity compared to short-reads across a large patient cohort.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Elrick, Hillary

Advisor dc:contributor.advisor

• Cortes Ciriano, Isidro

• bioinformatics
• cancer genomics
• long-read whole genome sequencing
• structural variants

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/d323ee04-834f-446e-ad73-7ddada068fc2/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.117082

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/382098