Rapid, ultra low coverage copy number profiling of cell-free DNA as a precision oncology screening strategy

Current cell-free DNA (cfDNA) next generation sequencing (NGS) precision oncology workflows are typically limited to targeted and/or disease-specific applications. In advanced cancer, disease burden and cfDNA tumor content are often elevated, yielding unique precision oncology opportunities. We sought to demonstrate the utility of a pan-cancer, rapid, inexpensive, whole genome NGS of cfDNA approach (PRINCe) as a precision oncology screening strategy via ultra-low coverage (~0.01x) tumor content determination through genome-wide copy number alteration (CNA) profiling. We applied PRINCe to a retrospective cohort of 124 cfDNA samples from 100 patients with advanced cancers, including 76 men with metastatic castration-resistant prostate cancer (mCRPC), enabling cfDNA tumor content approximation and actionable focal CNA detection, while facilitating concordance analyses between cfDNA and tissue-based NGS profiles and assessment of cfDNA alteration associations with mCRPC treatment outcomes. Therapeutically relevant focal CNAs were present in 42 (34%) cfDNA samples, including 36 of 93 (39%) mCRPC patient samples harboring AR amplification. PRINCe identified pre-treatment cfDNA CNA profiles facilitating disease monitoring. Combining PRINCe with routine targeted NGS of cfDNA enabled mutation and CNA assessment with coverages tuned to cfDNA tumor content. In mCRPC, genome-wide PRINCe cfDNA and matched tissue CNA profiles showed high concordance (median Pearson correlation = 0.87), and PRINCe detectable AR amplifications predicted reduced time on therapy, independent of therapy type (Kaplan-Meier log-rank test, chi-square = 24.9, p < 0.0001). Our screening approach enables robust, broadly applicable cfDNA-based precision oncology for patients with advanced cancer through scalable identification of therapeutically relevant CNAs and pre-/post-treatment genomic profiles, enabling cfDNA- or tissue-based precision oncology workflow optimization.

Oncotarget. 2017 Sep 22*** epublish ***

Daniel H Hovelson, Chia-Jen Liu, Yugang Wang, Qing Kang, James Henderson, Amy Gursky, Scott Brockman, Nithya Ramnath, John C Krauss, Moshe Talpaz, Malathi Kandarpa, Rashmi Chugh, Missy Tuck, Kirk Herman, Catherine S Grasso, Michael J Quist, Felix Y Feng, Christine Haakenson, John Langmore, Emmanuel Kamberov, Tim Tesmer, Hatim Husain, Robert J Lonigro, Dan Robinson, David C Smith, Ajjai S Alva, Maha H Hussain, Arul M Chinnaiyan, Muneesh Tewari, Ryan E Mills, Todd M Morgan, Scott A Tomlins

Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA., Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA., Department of Internal Medicine (Hematology/Oncology), University of Michigan Medical School, Ann Arbor, MI, USA., Division of Hematology-Oncology, University of California, Los Angeles and the Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA., Departments of Radiation Oncology, Urology, and Medicine, University of California at San Francisco, San Francisco, CA, USA., Takara Bio USA, Ann Arbor, MI, USA., Medical Oncology, University of California, San Diego Moore's Cancer Center, San Diego, CA, USA., Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.