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

Peter Ulz,Albert Wölfler,Charles E S Roberts,Thomas Bauernhofer,Qing Zhou,Jelena Belic,Armin Zebisch,Michael G Schimek,Edgar Petru,Heinz Sill,Samantha Perakis,John St John,Tina Moser,Christoph Bock,Armin Gerger,Isaac Lazzeri,Ellen Heitzer,Brandon White,Jochen B Geigl,Gunda Pristauz,Michael R Speicher

doi:10.1038/s41467-019-12714-4

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.

Highlights

Our approach allows identification of lineage-specific transcription factors (TFs) and profiling of individual TFs from cell-free DNA (cfDNA)
We demonstrate two relevant clinical applications: first, our TF-based cfDNA assays are capable of distinguishing between prostate adenocarcinoma and small-cell neuroendocrine prostate cancer, a distinction that has important therapeutic implications
We developed an approach and bioinformatics software pipeline to establish a metric, i.e., the accessibility score, for inferring TF binding from cfDNA in the blood, with relevance for clinical diagnostics and noninvasive tumor classification

Summary

Introduction

Our approach allows identification of lineage-specific TFs and profiling of individual TFs from cfDNA. We demonstrate two relevant clinical applications: first, our TF-based cfDNA assays are capable of distinguishing between prostate adenocarcinoma and small-cell neuroendocrine prostate cancer, a distinction that has important therapeutic implications. The large colon cancer cohort enabled us to accurately establish resolution limits and to explore the use of TF-based plasma analyses for detection of early cancer stages

Methods

Results

Conclusion