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

Significance The detection of aneuploidy in clinical samples can be critical for various diagnostic applications in cancer and can also inform cancer genetics. Next-generation sequencing protocols such as whole-genome and exome sequencing are typically used to detect aneuploidy in cancer samples, but amplicon-based protocols achieve high coverage depth at relatively low cost and can be used when only tiny amounts of DNA are available. In this paper, we describe new bioinformatic tools to detect aneuploidy using data generated from amplification with a single primer pair. This approach can be applied to 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. 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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