A framework for clinical cancer subtyping from nucleosome profiling of cell-free DNA

Anna-Lisa Doebley, Minjeong Ko, Hanna Liao, A. Eden Cruikshank, Katheryn Santos, Caroline Kikawa, Joseph B. Hiatt, Robert D. Patton, Navonil De Sarkar, Katharine A. Collier, Anna C. H. Hoge, Katharine Chen, Anat Zimmer, Zachary T. Weber, Mohamed Adil, Jonathan B. Reichel, Paz Polak, Viktor A. Adalsteinsson, Peter S. Nelson, David MacPherson, Heather A. Parsons, Daniel G. Stover and Gavin Ha ()
Additional contact information
Anna-Lisa Doebley: Fred Hutchinson Cancer Center
Minjeong Ko: Fred Hutchinson Cancer Center
Hanna Liao: University of Washington
A. Eden Cruikshank: Fred Hutchinson Cancer Center
Katheryn Santos: Dana-Farber Cancer Institute
Caroline Kikawa: University of Washington
Joseph B. Hiatt: Fred Hutchinson Cancer Center
Robert D. Patton: Fred Hutchinson Cancer Center
Navonil De Sarkar: Fred Hutchinson Cancer Center
Katharine A. Collier: Ohio State University Comprehensive Cancer Center
Anna C. H. Hoge: Fred Hutchinson Cancer Center
Katharine Chen: University of Washington
Anat Zimmer: Fred Hutchinson Cancer Center
Zachary T. Weber: Ohio State University Comprehensive Cancer Center
Mohamed Adil: Fred Hutchinson Cancer Center
Jonathan B. Reichel: Fred Hutchinson Cancer Center
Paz Polak: Mount Sinai
Viktor A. Adalsteinsson: Broad Institute of MIT and Harvard
Peter S. Nelson: Fred Hutchinson Cancer Center
David MacPherson: Fred Hutchinson Cancer Center
Heather A. Parsons: Dana-Farber Cancer Institute
Daniel G. Stover: Ohio State University Comprehensive Cancer Center
Gavin Ha: Fred Hutchinson Cancer Center

Nature Communications, 2022, vol. 13, issue 1, 1-18

Abstract: Abstract Cell-free DNA (cfDNA) has the potential to inform tumor subtype classification and help guide clinical precision oncology. Here we develop Griffin, a framework for profiling nucleosome protection and accessibility from cfDNA to study the phenotype of tumors using as low as 0.1x coverage whole genome sequencing data. Griffin employs a GC correction procedure tailored to variable cfDNA fragment sizes, which generates a better representation of chromatin accessibility and improves the accuracy of cancer detection and tumor subtype classification. We demonstrate estrogen receptor subtyping from cfDNA in metastatic breast cancer. We predict estrogen receptor subtype in 139 patients with at least 5% detectable circulating tumor DNA with an area under the receive operator characteristic curve (AUC) of 0.89 and validate performance in independent cohorts (AUC = 0.96). In summary, Griffin is a framework for accurate tumor subtyping and can be generalizable to other cancer types for precision oncology applications.

Date: 2022
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DOI: 10.1038/s41467-022-35076-w

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