Abstract 2516: A bioinformatics framework for high-sensitivity detection and monitoring of oncogenic gene fusions in plasma cfDNA
Authors: Juber Patel, Maysun Hasan, Fanli Meng, Xiaohong Jing, Grittney Tam, Ian Johnson, Youyun Zheng, Chaitanya Bandlamudi, Caitlin Stewart, Helen Won, Oliver Hampton, Alison Schram, Ezra Rosen, Alexander Drilon, Anna Varghese, David Hyman, Dana Tsui, Brian Houck-Loomis, Michael Berger
Journal: Cancer Research (2019)

Abstract Gene fusions represent key oncogenic driver alterations in many solid tumors that can be targeted therapeutically. Beyond FDA-approved drugs targeting ALK and ROS1 fusions in lung cancer, investigational agents targeting gene fusions involving RET, NTRK1/2/3, and FGFR2/3 have elicited significant and durable responses in a wide range of cancer types. Cell-free DNA (cfDNA) profiling provides a transformative opportunity to non-invasively and longitudinally monitor responses to these investigational therapies and identify acquired mutations that confer drug resistance. We have developed a pan-cancer, high-sensitivity NGS cancer assay (MSK-ACCESS, presented previously) to detect and monitor somatic mutations in plasma cfDNA. Here, we present an improved bioinformatics pipeline that detects gene fusions and achieves greater sensitivity and accuracy for detecting point mutations and indels. MSK-ACCESS uses ultra-high depth sequencing (~20,000x) coupled with duplex unique molecular indexing (UMI) and dual sample barcodes to achieve high sensitivity through background error suppression. The panel was designed to capture key exons and domains of 129 cancer genes as well as introns harboring recurrent breakpoints in 10 commonly rearranged genes. We have now developed a bioinformatics module for sensitive detection of structural variation from cfDNA, leveraging the high-depth sequencing data and intronic coverage. We have also added a statistical polishing method that assigns confidence to detected somatic mutations by building frequency distributions for those mutations from a large set of normal samples that has been processed with the same laboratory protocol and bioinformatics pipeline as the patient samples. In parallel, we have further improved the wet lab protocols to address challenges of capturing sufficient numbers of cfDNA molecules and removing noise, both required for detection of somatic mutations with high sensitivity and specificity. Using this method, we have successfully detected druggable fusions in plasma cfDNA from patients spanning a diverse set of cancer types. Through longitudinal cfDNA collection efforts embedded within early-phase clinical trials, we demonstrate that the levels of oncogenic driver fusions in cfDNA may scale with the burden of disease. Moreover, the ability to simultaneously call high-confidence mutations at low allele fractions has revealed novel acquired mutations indicative of polyclonal resistance to investigational targeted therapies. Citation Format: Juber Patel, Maysun Hasan, Fanli Meng, Xiaohong Jing, Grittney Tam, Ian Johnson, Youyun Zheng, Chaitanya Bandlamudi, Caitlin Stewart, Helen Won, Oliver Hampton, Alison Schram, Ezra Rosen, Alexander Drilon, Anna Varghese, David Hyman, Dana Tsui, Brian Houck-Loomis, Michael Berger. A bioinformatics framework for high-sensitivity detection and monitoring of oncogenic gene fusions in plasma cfDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2516.