Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology

Kate E. Stanley, Tatjana Jatsenko, Stefania Tuveri, Dhanya Sudhakaran, Lore Lannoo, Kristel Calsteren, Marie Borre, Ilse Parijs, Leen Coillie, Kris Bogaert, Rodrigo Almeida Toledo, Liesbeth Lenaerts, Sabine Tejpar, Kevin Punie, Laura Y. Rengifo, Peter Vandenberghe, Bernard Thienpont and Joris Robert Vermeesch ()
Additional contact information
Kate E. Stanley: Laboratory for Cytogenetics and Genome Research, KU Leuven
Tatjana Jatsenko: Laboratory for Cytogenetics and Genome Research, KU Leuven
Stefania Tuveri: Laboratory for Cytogenetics and Genome Research, KU Leuven
Dhanya Sudhakaran: Laboratory for Cytogenetics and Genome Research, KU Leuven
Lore Lannoo: University Hospitals Leuven
Kristel Calsteren: University Hospitals Leuven
Marie Borre: Laboratory for Functional Epigenetics, KU Leuven
Ilse Parijs: University Hospitals Leuven
Leen Coillie: University Hospitals Leuven
Kris Bogaert: University Hospitals Leuven
Rodrigo Almeida Toledo: Vall d’Hebron Institute of Oncology
Liesbeth Lenaerts: KU Leuven
Sabine Tejpar: KU Leuven
Kevin Punie: University Hospitals Leuven
Laura Y. Rengifo: Laboratory of Genetics of Malignant Diseases, KU Leuven
Peter Vandenberghe: Laboratory of Genetics of Malignant Diseases, KU Leuven
Bernard Thienpont: Laboratory for Functional Epigenetics, KU Leuven
Joris Robert Vermeesch: Laboratory for Cytogenetics and Genome Research, KU Leuven

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Circulating cell-free DNA (cfDNA) fragments have characteristics that are specific to the cell types that release them. Current methods for cfDNA deconvolution typically use disease tailored marker selection in a limited number of bulk tissues or cell lines. Here, we utilize single cell transcriptome data as a comprehensive cellular reference set for disease-agnostic cfDNA cell-of-origin analysis. We correlate cfDNA-inferred nucleosome spacing with gene expression to rank the relative contribution of over 490 cell types to plasma cfDNA. In 744 healthy individuals and patients, we uncover cell type signatures in support of emerging disease paradigms in oncology and prenatal care. We train predictive models that can differentiate patients with colorectal cancer (84.7%), early-stage breast cancer (90.1%), multiple myeloma (AUC 95.0%), and preeclampsia (88.3%) from matched controls. Importantly, our approach performs well in ultra-low coverage cfDNA datasets and can be readily transferred to diverse clinical settings for the expansion of liquid biopsy.

Date: 2024
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DOI: 10.1038/s41467-024-46435-0

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