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Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA
MOTIVATION: Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensiv...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468241/ https://www.ncbi.nlm.nih.gov/pubmed/30759232 http://dx.doi.org/10.1093/nar/gkz067 |
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author | Gawroński, Alexander R Lin, Yen-Yi McConeghy, Brian LeBihan, Stephane Asghari, Hossein Koçkan, Can Orabi, Baraa Adra, Nabil Pili, Roberto Collins, Colin C Sahinalp, S Cenk Hach, Faraz |
author_facet | Gawroński, Alexander R Lin, Yen-Yi McConeghy, Brian LeBihan, Stephane Asghari, Hossein Koçkan, Can Orabi, Baraa Adra, Nabil Pili, Roberto Collins, Colin C Sahinalp, S Cenk Hach, Faraz |
author_sort | Gawroński, Alexander R |
collection | PubMed |
description | MOTIVATION: Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the ‘liquid biopsy’ possible, which involves sampling a patient’s blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable. RESULTS: Here we introduce SViCT , a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT was able to detect all known SVs in two real cfDNA reference datasets (at 0.6–5% ctDNA) and predict a novel structural variant in a prostate cancer cohort. AVAILABILITY: SViCT is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca |
format | Online Article Text |
id | pubmed-6468241 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-64682412019-04-22 Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA Gawroński, Alexander R Lin, Yen-Yi McConeghy, Brian LeBihan, Stephane Asghari, Hossein Koçkan, Can Orabi, Baraa Adra, Nabil Pili, Roberto Collins, Colin C Sahinalp, S Cenk Hach, Faraz Nucleic Acids Res Methods Online MOTIVATION: Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the ‘liquid biopsy’ possible, which involves sampling a patient’s blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable. RESULTS: Here we introduce SViCT , a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT was able to detect all known SVs in two real cfDNA reference datasets (at 0.6–5% ctDNA) and predict a novel structural variant in a prostate cancer cohort. AVAILABILITY: SViCT is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca Oxford University Press 2019-04-23 2019-02-13 /pmc/articles/PMC6468241/ /pubmed/30759232 http://dx.doi.org/10.1093/nar/gkz067 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Online Gawroński, Alexander R Lin, Yen-Yi McConeghy, Brian LeBihan, Stephane Asghari, Hossein Koçkan, Can Orabi, Baraa Adra, Nabil Pili, Roberto Collins, Colin C Sahinalp, S Cenk Hach, Faraz Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA |
title | Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA |
title_full | Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA |
title_fullStr | Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA |
title_full_unstemmed | Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA |
title_short | Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA |
title_sort | structural variation and fusion detection using targeted sequencing data from circulating cell free dna |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468241/ https://www.ncbi.nlm.nih.gov/pubmed/30759232 http://dx.doi.org/10.1093/nar/gkz067 |
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