Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection

Peter Ulz, Samantha Perakis, Qing Zhou, Tina Moser, Jelena Belic, Isaac Lazzeri, Albert Wölfler, Armin Zebisch, Armin Gerger, Gunda Pristauz, Edgar Petru, Brandon White, Charles E. S. Roberts, John St. John, Michael G. Schimek, Jochen B. Geigl, Thomas Bauernhofer, Heinz Sill, Christoph Bock, Ellen Heitzer () and Michael R. Speicher ()
Additional contact information
Peter Ulz: Medical University of Graz
Samantha Perakis: Medical University of Graz
Qing Zhou: Medical University of Graz
Tina Moser: Medical University of Graz
Jelena Belic: Medical University of Graz
Isaac Lazzeri: Medical University of Graz
Albert Wölfler: Medical University of Graz
Armin Zebisch: Medical University of Graz
Armin Gerger: Medical University of Graz
Gunda Pristauz: Medical University of Graz
Edgar Petru: Medical University of Graz
Brandon White: Freenome
Charles E. S. Roberts: Freenome
John St. John: Freenome
Michael G. Schimek: Medical University of Graz
Jochen B. Geigl: Medical University of Graz
Thomas Bauernhofer: Medical University of Graz
Heinz Sill: Medical University of Graz
Christoph Bock: CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences
Ellen Heitzer: Medical University of Graz
Michael R. Speicher: Medical University of Graz

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. Here we develop and validate a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyze whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-12714-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12714-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-12714-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().