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

Doebley A-L, 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, Gavin Ha.
Nature Communications 13, 7475 (2022).

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.