High resolution copy number inference in cancer using short-molecule nanopore sequencing.


Journal

Nucleic acids research
ISSN: 1362-4962
Titre abrégé: Nucleic Acids Res
Pays: England
ID NLM: 0411011

Informations de publication

Date de publication:
02 12 2021
Historique:
accepted: 09 09 2021
revised: 19 07 2021
received: 02 02 2021
pubmed: 23 9 2021
medline: 11 1 2022
entrez: 22 9 2021
Statut: ppublish

Résumé

Genome copy number is an important source of genetic variation in health and disease. In cancer, Copy Number Alterations (CNAs) can be inferred from short-read sequencing data, enabling genomics-based precision oncology. Emerging Nanopore sequencing technologies offer the potential for broader clinical utility, for example in smaller hospitals, due to lower instrument cost, higher portability, and ease of use. Nonetheless, Nanopore sequencing devices are limited in the number of retrievable sequencing reads/molecules compared to short-read sequencing platforms, limiting CNA inference accuracy. To address this limitation, we targeted the sequencing of short-length DNA molecules loaded at optimized concentration in an effort to increase sequence read/molecule yield from a single nanopore run. We show that short-molecule nanopore sequencing reproducibly returns high read counts and allows high quality CNA inference. We demonstrate the clinical relevance of this approach by accurately inferring CNAs in acute myeloid leukemia samples. The data shows that, compared to traditional approaches such as chromosome analysis/cytogenetics, short molecule nanopore sequencing returns more sensitive, accurate copy number information in a cost effective and expeditious manner, including for multiplex samples. Our results provide a framework for short-molecule nanopore sequencing with applications in research and medicine, which includes but is not limited to, CNAs.

Identifiants

pubmed: 34551429
pii: 6374175
doi: 10.1093/nar/gkab812
pmc: PMC8643650
doi:

Substances chimiques

DNA 9007-49-2

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e124

Subventions

Organisme : NCI NIH HHS
ID : R01 CA190261
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA253481
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Organisme : NCI NIH HHS
ID : R50 CA243890
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA045508
Pays : United States

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Auteurs

Timour Baslan (T)

Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Sam Kovaka (S)

Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

Fritz J Sedlazeck (FJ)

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.

Yanming Zhang (Y)

Cytogenetics Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Robert Wappel (R)

Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

Sha Tian (S)

Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Scott W Lowe (SW)

Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Howard Hughes Medical Institute, Chevy Chase, MD, USA.

Sara Goodwin (S)

Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

Michael C Schatz (MC)

Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Department of Biology, Johns Hopkins University, Baltimore, MD, USA.

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Classifications MeSH