We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.

Cmero, M., Yuan, K., Ong, C., Schroder, J., Adams, D., Anur, P., et al. (2020). Inferring structural variant cancer cell fraction. NATURE COMMUNICATIONS, 11(1) [10.1038/s41467-020-14351-8].

Inferring structural variant cancer cell fraction

Gambacorti-Passerini C.;
2020

Abstract

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.
Articolo in rivista - Articolo scientifico
Algorithms; Computational Biology; Computer Simulation; DNA Copy Number Variations; Female; Gene Frequency; Genome, Human; Humans; Liver Neoplasms; Male; Neoplasms; Ovarian Neoplasms; Pancreatic Neoplasms; Prostatic Neoplasms; Sensitivity and Specificity; Whole Genome Sequencing
English
2020
11
1
730
none
Cmero, M., Yuan, K., Ong, C., Schroder, J., Adams, D., Anur, P., et al. (2020). Inferring structural variant cancer cell fraction. NATURE COMMUNICATIONS, 11(1) [10.1038/s41467-020-14351-8].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/473270
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