Assessing copy number changes in High Grade Serous Ovarian Cancer with a shallow whole genome workflow
Year: 2024
Type: article
Abstract: Introduction: Genomics has underpinned significant improvements in tumour classification and patient survival in phenotypes like breast cancer which can be assessed by microarray. However patient survival for phenotypes dominated by copy number variation, like High Grade Serous Ovarian Cancer (HGSOC), has remained refractory. Shallow whole genome sequencing, where multiple samples are multiplexed in a single sequencing lane, provides a potentially cost-effective means of assessment for precision medicine treatments based on chromosomal instability signatures (CIN). Methods: Here we describe a bioinformatic workflow for processing these samples at UNSW in a high throughput manner, capable of dealing with over a thousand samples in a single run. Firstly, this involves alignment of the sequences on high performance computers like UNSW s katana or the National Supercomputer GADI. Postprocessing of bam files to produce segmented absolute copy number objects, while still reasonably computationally intensive, can then be performed on a high-end personal computer when R scripts are deployed using Snakemake. This area is still under development as we move away from normalization using allele frequencies to more reliable methods. Further postprocessing on the reduced dataset to assign chromosomal signatures and do further investigation can then be performed in R. Throughout this process, quality controls are deployed and continue to be developed, including dimension reduction techniques such as umap. Results and Conclusion: Assignment of CIN using non-negative matrix factorisation of copy number characteristics indicate more structure in HGSOC than the accepted Homologous Recombination Deficient (HRD)/nonHRD dichotomy. Further research should enable better assessment and precision medicine treatments.
Author: Wouters, Merridee
Institution: UNSW Sydney
Language: English
Subfield: Reproductive Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6893315693
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