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

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

doi:10.18632/oncotarget.21163

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

Oncotarget. 2017 Sep 22;8(52):89848-89866. doi: 10.18632/oncotarget.21163. eCollection 2017 Oct 27.

Authors

Daniel H Hovelson^{1

2}, Chia-Jen Liu^{1

3}, Yugang Wang⁴, Qing Kang⁵, James Henderson⁴, Amy Gursky⁴, Scott Brockman¹, Nithya Ramnath⁵, John C Krauss⁵, Moshe Talpaz⁵, Malathi Kandarpa⁵, Rashmi Chugh⁵, Missy Tuck⁵, Kirk Herman⁵, Catherine S Grasso^{6

7}, Michael J Quist^{6

7}, Felix Y Feng⁸, Christine Haakenson⁹, John Langmore⁹, Emmanuel Kamberov⁹, Tim Tesmer⁹, Hatim Husain¹⁰, Robert J Lonigro^{1

3}, Dan Robinson^{1

3

11}, David C Smith^{5

11}, Ajjai S Alva^{5

11}, Maha H Hussain^{5

11

12}, Arul M Chinnaiyan^{1

3

11

6}, Muneesh Tewari^{2

5

13

14

11

15}, Ryan E Mills^{2

14}, Todd M Morgan^{1

4

11}, Scott A Tomlins^{1

3

4

11}

Affiliations

¹ Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.
² Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
³ Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA.
⁴ Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA.
⁵ Department of Internal Medicine (Hematology/Oncology), University of Michigan Medical School, Ann Arbor, MI, USA.
⁶ Division of Hematology-Oncology, University of California, Los Angeles and the Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA.
⁷ The Parker Institute of Cancer Immunotherapy, San Francisco, CA, USA.
⁸ Departments of Radiation Oncology, Urology, and Medicine, University of California at San Francisco, San Francisco, CA, USA.
⁹ Takara Bio USA, Ann Arbor, MI, USA.
¹⁰ Medical Oncology, University of California, San Diego Moore's Cancer Center, San Diego, CA, USA.
¹¹ Department of Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA.
¹² Present address: Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
¹³ Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI, USA.
¹⁴ Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA.
¹⁵ Department of Biointerfaces Institute, University of Michigan Medical School, Ann Arbor, MI, USA.

Abstract

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.

Keywords: cell-free DNA; copy-number analysis; precision oncology; prostate cancer; whole genome sequencing.

Abstract

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