Single molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

Somatic mutations are a hallmark of tumorigenesis and may be useful for non-invasive diagnosis of cancer. We analyzed whole-genome sequencing (WGS) data from 2,511 individuals in the Pan-Cancer Analysis of Whole Genomes (PCAWG) study as well as 489 individuals from four prospective cohorts and found distinct regional and mutation type specific frequencies in tissue and cell-free DNA (cfDNA) of cancer patients that were associated with replication timing and other chromatin features. A machine learning model using genome-wide mutational profiles combined with other features and followed by CT imaging detected >90% of lung cancer patients, including those with stage I and II disease. The fixed model was validated in an independent cohort, detected patients with cancer earlier than standard approaches, and could be used to monitor response to therapy. This approach lays the groundwork for non-invasive cancer detection using genome-wide mutation features that may facilitate cancer screening and monitoring.EGA study EGAS00001007248

体细胞突变是肿瘤发生的标志性特征，可用于癌症的非侵入性诊断。本研究对泛癌全基因组分析（PCAWG）队列中2511名个体的全基因组测序（WGS）数据，以及4项前瞻性队列的489名个体的数据进行分析，发现癌症患者组织与无细胞DNA（cfDNA）中存在显著的区域特异性与突变类型特异性频率分布特征，且该特征与复制时序及其他染色质特征相关。本研究构建的机器学习模型结合全基因组突变谱与其他特征，并辅以CT成像，可检出超过90%的肺癌患者，其中包括I期与II期肺癌患者。该定型模型在独立队列中得到验证，其癌症检出时间早于标准检测手段，且可用于监测治疗响应情况。该研究为基于全基因组突变特征的非侵入性癌症检测奠定了基础，有望推动癌症筛查与监测工作的开展。EGA研究 EGAS00001007248