Detection of aneuploidy in patients with cancer using amplicon sequencing - 11th International Symposium on Circulating Nucleic Acids in Plasma and Serum (CNAPS)

Christopher Douville ^1,3,4 Bert Vogelstein ^1,2,3,4 Kenneth Kinzler ^1,3,4 Nickolas Papadopoulos ^1,3,4

¹Oncology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
²Howard Hughes Medical Institute, Johns Hopkins Medical Institutions, Baltimore, MD, USA
³Ludwig Center, Johns Hopkins Medical Institutions, Baltimore, MD, USA
⁴Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medical Institutions, Baltimore, MD, USA

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 WALDO for 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 as well as 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 (SNPs) within the amplified LINEs, WALDO concomitantly assesses allelic imbalances, microsatellite instability and sample identification. WALDO can be used on samples containing only a few ng of DNA and as little as 1% neoplastic content and has a variety of applications in cancer diagnostics and forensic science. Recently, we have made modifications to our approach that resulted in a substantial improvement in sensitivity.