Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA

Jonathan C. M. Wan, Dennis Stephens, Lingqi Luo, James R. White, Caitlin M. Stewart, Benoît Rousseau, Dana W. Y. Tsui and Luis A. Diaz ()
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Jonathan C. M. Wan: Memorial Sloan Kettering Cancer Center
Dennis Stephens: Memorial Sloan Kettering Cancer Center
Lingqi Luo: Memorial Sloan Kettering Cancer Center
James R. White: Memorial Sloan Kettering Cancer Center
Caitlin M. Stewart: Memorial Sloan Kettering Cancer Center
Benoît Rousseau: Memorial Sloan Kettering Cancer Center
Dana W. Y. Tsui: Memorial Sloan Kettering Cancer Center
Luis A. Diaz: Memorial Sloan Kettering Cancer Center

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract Mutational signatures accumulate in somatic cells as an admixture of endogenous and exogenous processes that occur during an individual’s lifetime. Since dividing cells release cell-free DNA (cfDNA) fragments into the circulation, we hypothesize that plasma cfDNA might reflect mutational signatures. Point mutations in plasma whole genome sequencing (WGS) are challenging to identify through conventional mutation calling due to low sequencing coverage and low mutant allele fractions. In this proof of concept study of plasma WGS at 0.3–1.5x coverage from 215 patients and 227 healthy individuals, we show that both pathological and physiological mutational signatures may be identified in plasma. By applying machine learning to mutation profiles, patients with stage I-IV cancer can be distinguished from healthy individuals with an Area Under the Curve of 0.96. Interrogating mutational processes in plasma may enable earlier cancer detection, and might enable the assessment of cancer risk and etiology.

Date: 2022
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DOI: 10.1038/s41467-022-32598-1

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