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Abujudeh S, Zeki SS, van Lanschot MCJ, Pusung M, Weaver JMJ, Li X, Noorani A, Metz AJ, Bornschein J, Bower L, Miremadi A, Fitzgerald RC, Morrissey ER, Lynch AG. Low-cost and clinically applicable copy number profiling using repeat DNA. BMC Genomics 2022; 23:599. [PMID: 35978291 PMCID: PMC9386984 DOI: 10.1186/s12864-022-08681-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 06/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND 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.
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Affiliation(s)
- Sam Abujudeh
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
| | - Sebastian S Zeki
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK. .,Department of Gastroenterology, Guy's and St Thomas' NHS Trust, London, SE1 7EH, UK.
| | | | - Mark Pusung
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK
| | - Jamie M J Weaver
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK.,Department of Medical Oncology, The Christie NHS Foundation Trust, Manchester, M20 4TX, UK
| | - Xiaodun Li
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK
| | - Ayesha Noorani
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK
| | - Andrew J Metz
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK
| | - Jan Bornschein
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK
| | - Lawrence Bower
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Ahmad Miremadi
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK
| | - Rebecca C Fitzgerald
- Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK.
| | - Edward R Morrissey
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. .,Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
| | - Andy G Lynch
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. .,School of Mathematics and Statistics/School of Medicine, University of St Andrews, St Andrews, UK.
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