ArticleProceedings of the National Academy of Sciences of the United States of America2018
Detection of aneuploidy in patients with cancer through amplification of long interspersed nucleotide elements (LINEs).
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
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Authors and funding
10 authors.
Funding
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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