2021
DOI: 10.1038/s41467-021-26698-7
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Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data

Abstract: Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform resea… Show more

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Cited by 17 publications
(12 citation statements)
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“…PyClone-VI (version 0.1.1) 60 was used to estimate the number of clones and clonal architectures based on VAFs of somatic mutations from the results of GATK Mutect2. As recommended by the previous report 61 , CN profiles and tumor purity were calculated by FACETS (version 0.6.2) 62 with the parameter cval = 400 of the proc-Sample function as input of PyClone-VI. For male specimens, minor CNs of chromosome X were set to zero.…”
Section: Methodsmentioning
confidence: 99%
“…PyClone-VI (version 0.1.1) 60 was used to estimate the number of clones and clonal architectures based on VAFs of somatic mutations from the results of GATK Mutect2. As recommended by the previous report 61 , CN profiles and tumor purity were calculated by FACETS (version 0.6.2) 62 with the parameter cval = 400 of the proc-Sample function as input of PyClone-VI. For male specimens, minor CNs of chromosome X were set to zero.…”
Section: Methodsmentioning
confidence: 99%
“…3) patients with high-quality cancer cell fraction estimation. These CCF estimations were obtained using CCube 33, 34 , which has been shown to be robust across several benchmarks 35, 36 . 4) cancer types with at least 100 samples.…”
Section: Resultsmentioning
confidence: 99%
“…Since somatic copy number alteration (SCNA) detection tools are prone to high false positive rates as well as issues with precision and accuracy ( 59 , 60 ), we also performed SCNA calling using CNVkit ( 61 ), which combines all normal samples into a pooled reference to increase performance. In contrast to Sequenza, this method identified a greater number of SCNA in primary TNBC relative to BrM, showing that, as a collective group, primary TNBC samples harbored 27 significant amplicons and 17 deleted regions, whereas BrM had 8 significant amplicons and 4 regions of deletion ( q < 0.25; Figures 1E, F ) .…”
Section: Resultsmentioning
confidence: 99%