Inferring expressed genes by whole-genome sequencing of plasma DNA

Michael Speicher and colleagues analyze plasma DNA whole-genome sequencing data from healthy donors and patients with cancer to infer nucleosome positioning on the basis of read depth coverage patterns. They use this approach to accurately predict expression of cancer driver genes from circulating tumor DNA in regions with somatic copy number gains. The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing of plasma DNA and identified two discrete regions at transcription start sites (TSSs) where nucleosome occupancy results in different read depth coverage patterns for expressed and silent genes. By employing machine learning for gene classification, we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells. In patients with cancer having metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We were able to determine the expressed isoform of genes with several TSSs, as confirmed by RNA-seq analysis of the matching primary tumor. Our analyses provide functional information about cells releasing their DNA into the circulation.