Calibration of cell-free DNA measurements by next-generation sequencing

Author:

Hoerres Derek1ORCID,Dai Qunsheng12,Elmore Sandra12,Sheth Siddharth23,Gupta Gaorav P24,Kumar Sunil24,Gulley Margaret L12

Affiliation:

1. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill , Chapel Hill, NC , US

2. Lineberger Comprehensive Cancer Center , Chapel Hill, NC , US

3. Division of Oncology, University of North Carolina at Chapel Hill , Chapel Hill, NC , US

4. Department of Radiation Oncology, University of North Carolina at Chapel Hill , Chapel Hill, NC , US

Abstract

Abstract Objectives Accurate monitoring of disease burden depends on accurate disease marker quantification. Although next-generation sequencing (NGS) is a promising technology for noninvasive monitoring, plasma cell-free DNA levels are often reported in misleading units that are confounded by non–disease-related factors. We proposed a novel strategy for calibrating NGS assays using spiked normalizers to improve precision and to promote standardization and harmonization of analyte concentrations. Methods In this study, we refined our NGS protocol to calculate absolute analyte concentrations to (1) adjust for assay efficiency, as judged by recovery of spiked synthetic normalizer DNAs, and (2) calibrate NGS values against droplet digital polymerase chain reaction (ddPCR). As a model target, we chose the Epstein-Barr virus (EBV) genome. In patient (n = 12) and mock (n = 12) plasmas, NGS and 2 EBV ddPCR assays were used to report EBV load in copies per mL of plasma. Results Next-generation sequencing was equally sensitive to ddPCR, with improved linearity when NGS values were normalized for spiked DNA read counts (R2 = 0.95 for normalized vs 0.91 for raw read concentrations). Linearity permitted NGS calibration to each ddPCR assay, achieving equivalent concentrations (copies/mL). Conclusions Our novel strategy for calibrating NGS assays suggests potential for a universal reference material to overcome biological and preanalytical variables hindering traditional NGS strategies for quantifying disease burden.

Funder

National Center for Advancing Translational Sciences

National Cancer Institute

University of North Carolina

Translational Science Center

NIH

Department of Pathology and Laboratory Medicine

Publisher

Oxford University Press (OUP)

Subject

General Medicine

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