Detection of aneuploidy in patients with cancer through amplification of long interspersed nucleotide elements (LINEs)

Research output: Contribution to journalArticle

Abstract

Aneuploidy is a feature of most cancer cells, and a myriad of approaches have been developed to detect it in clinical samples. We previously described primers that could be used to amplify ~38,000 unique long interspersed nucleotide elements (LINEs) from throughout the genome. Here we have developed an approach to evaluate the sequencing data obtained from these amplicons. This approach, called Within-Sample AneupLoidy DetectiOn (WALDO), employs supervised machine learning to detect the small changes in multiple chromosome arms that are often present in cancers. We used WALDO to search for chromosome arm gains and losses in 1,677 tumors and in 1,522 liquid biopsies of blood from cancer patients or normal individuals. Aneuploidy was detected in 95% of cancer biopsies and in 22% of liquid biopsies. Using single-nucleotide polymorphisms within the amplified LINEs, WALDO concomitantly assesses allelic imbalances, microsatellite instability, and sample identification. WALDO can be used on samples containing only a few nanograms of DNA and as little as 1% neoplastic content and has a variety of applications in cancer diagnostics and forensic science.

Original languageEnglish (US)
Pages (from-to)1871-1876
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number8
DOIs
StatePublished - Feb 20 2018

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Long Interspersed Nucleotide Elements
Aneuploidy
Neoplasms
Biopsy
Chromosomes
Allelic Imbalance
Forensic Sciences
Microsatellite Instability
Single Nucleotide Polymorphism
Genome
DNA

Keywords

  • Aneuploidy
  • Circulating tumor DNA
  • Early cancer detection
  • Liquid biopsy

ASJC Scopus subject areas

  • General

Cite this

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abstract = "Aneuploidy is a feature of most cancer cells, and a myriad of approaches have been developed to detect it in clinical samples. We previously described primers that could be used to amplify ~38,000 unique long interspersed nucleotide elements (LINEs) from throughout the genome. Here we have developed an approach to evaluate the sequencing data obtained from these amplicons. This approach, called Within-Sample AneupLoidy DetectiOn (WALDO), employs supervised machine learning to detect the small changes in multiple chromosome arms that are often present in cancers. We used WALDO to search for chromosome arm gains and losses in 1,677 tumors and in 1,522 liquid biopsies of blood from cancer patients or normal individuals. Aneuploidy was detected in 95{\%} of cancer biopsies and in 22{\%} of liquid biopsies. Using single-nucleotide polymorphisms within the amplified LINEs, WALDO concomitantly assesses allelic imbalances, microsatellite instability, and sample identification. WALDO can be used on samples containing only a few nanograms of DNA and as little as 1{\%} neoplastic content and has a variety of applications in cancer diagnostics and forensic science.",
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author = "Christopher Douville and Simeon Springer and Isaac Kinde and Cohen, {Joshua D.} and Hruban, {Ralph H} and O'Broin-Lennon, {Anne Marie} and Nickolas Papadopoulos and Kinzler, {Kenneth W} and Bert Vogelstein and Rachel Karchin",
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AU - Douville, Christopher

AU - Springer, Simeon

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AU - Cohen, Joshua D.

AU - Hruban, Ralph H

AU - O'Broin-Lennon, Anne Marie

AU - Papadopoulos, Nickolas

AU - Kinzler, Kenneth W

AU - Vogelstein, Bert

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