Inferring expressed genes by whole-genome sequencing of plasma DNA

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 (WGS) of plasma DNA and identified two discrete regions at transcription start sites (TSS) where the nucleosome occupancy results in different read-depth coverage patterns in 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 cancer patients with metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We could even determine the expressed isoform of genes with several TSSs as confirmed by RNA-Seq of the matching primary tumor. Our analyses provide functional information about the cells releasing their DNA into the circulation.EGA study EGAS00001001754

血浆中无细胞DNA（cell-free DNA, cfDNA）的分析是医学领域快速发展的前沿研究方向。cfDNA主要由发生凋亡的细胞释放进入血液循环、并受核小体保护的DNA片段构成。我们对血浆DNA开展全基因组测序（whole-genome sequencing, WGS），在转录起始位点（transcription start sites, TSS）处发现两个离散区域：核小体的占据状态会在表达基因与沉默基因中形成截然不同的读深度覆盖模式。通过机器学习开展基因分类分析，我们发现健康供体的血浆DNA读深度覆盖模式能够反映造血细胞的基因表达特征。在罹患转移性疾病的癌症患者中，我们可高精度对携带体细胞拷贝数增加区域的表达型癌症驱动基因进行分类。对于拥有多个转录起始位点的基因，我们甚至能够确定其表达亚型，该结论经匹配原发肿瘤的RNA测序（RNA-Seq）验证得以确认。本研究的分析可为释放DNA进入血液循环的细胞提供功能层面的相关信息。EGA研究EGAS00001001754