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dc.contributor.authorAbujudeh, Sam
dc.contributor.authorZeki, Sebastian
dc.contributor.authorvan Lanshot, Meta
dc.contributor.authorPusang, M
dc.contributor.authorWeaver, Jamie
dc.contributor.authorli, X
dc.contributor.authorNoorani, Ayesha
dc.contributor.authorMetz, Andrew
dc.contributor.authorBornschein, Jan
dc.contributor.authorBower, Lawrence
dc.contributor.authorMiremedi, Ahmed
dc.contributor.authorFitzgerald, Rebecca
dc.contributor.authorMorrissey, Ed
dc.contributor.authorLynch, Andy
dc.identifier.citationAbujudeh , S , Zeki , S , van Lanshot , M , Pusang , M , Weaver , J , li , X , Noorani , A , Metz , A , Bornschein , J , Bower , L , Miremedi , A , Fitzgerald , R , Morrissey , E & Lynch , A 2022 , ' Low-cost and clinically applicable copy number profiling using repeat DNA ' , BMC Genomics , vol. 23 , 599 .
dc.identifier.otherPURE: 280336845
dc.identifier.otherPURE UUID: 9cce626a-4804-447f-a746-c1a298e4959b
dc.identifier.otherORCID: /0000-0002-7876-7338/work/117568037
dc.identifier.otherScopus: 85136026034
dc.identifier.otherWOS: 000842092000002
dc.descriptionFunding for sample sequencing was through the Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium as part of the International Cancer Genome Consortium and was funded by a programme grant from Cancer Research UK (RG66287). SA was funded by Wellcome Trust award (RG73199). ERM was funded by MRC Computational Biology Fellowship (MC_UU_12025, MRC Strategic Alliance Funding: MRC Weatherall Institute of Molecular Medicine).en
dc.description.abstractBackground Somatic copy number alterations (SCNAs) are an important class of genomic alteration in cancer. They are frequently observed in cancer samples, with studies showing that, on average, SCNAs affect 34% of a cancer cell’s genome. Furthermore, SCNAs have been shown to be major drivers of tumour development and have been associated with response to therapy and prognosis. Large-scale cancer genome studies suggest that tumours are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Despite the frequency of SCNAs and their clinical relevance, the use of genomics assays in the clinic is biased towards targeted gene panels, which identify SNVs but provide limited scope to detect SCNAs throughout the genome. There is a need for a comparably low-cost and simple method for high-resolution SCNA profiling. Results We present conliga, a fully probabilistic method that infers SCNA profiles from a low-cost, simple, and clinically-relevant assay (FAST-SeqS). When applied to 11 high-purity oesophageal adenocarcinoma samples, we obtain good agreement (Spearman’s rank correlation coefficient, rs=0.94) between conliga’s inferred SCNA profiles using FAST-SeqS data (approximately £14 per sample) and those inferred by ASCAT using high-coverage WGS (gold-standard). We find that conliga outperforms CNVkit (rs=0.89), also applied to FAST-SeqS data, and is comparable to QDNAseq (rs=0.96) applied to low-coverage WGS, which is approximately four-fold more expensive, more laborious and less clinically-relevant. By performing an in silico dilution series experiment, we find that conliga is particularly suited to detecting SCNAs in low tumour purity samples. At two million reads per sample, conliga is able to detect SCNAs in all nine samples at 3% tumour purity and as low as 0.5% purity in one sample. Crucially, we show that conliga’s hidden state information can be used to decide when a sample is abnormal or normal, whereas CNVkit and QDNAseq cannot provide this critical information. Conclusions We show that conliga provides high-resolution SCNA profiles using a convenient, low-cost assay. We believe conliga makes FAST-SeqS a more clinically valuable assay as well as a useful research tool, enabling inexpensive and fast copy number profiling of pre-malignant and cancer samples.
dc.relation.ispartofBMC Genomicsen
dc.rightsCopyright © The Author(s). 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
dc.subjectSomatic copy number alterationsen
dc.subjectCopy number profilingen
dc.subjectOesophageal adenocarcinomaen
dc.subjectBarrett's oesophagusen
dc.subjectTumour purityen
dc.subjectBayesian nonparametricsen
dc.subjectProbabilistic modelen
dc.subjectSticky HDP-HMMen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleLow-cost and clinically applicable copy number profiling using repeat DNAen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.description.statusPeer revieweden

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