Biological background of the genomic variations of cf-DNA in healthy individuals. (23rd November 2018) - Liu, J; Chen, X; Wang, J; Zhou, S; Wang, C L; Ye, M Z; Wang, X Y; Song, Y; Wang, Y Q; Zhang, L T; Wu, R H; Yang, H M; et al.
Abstract: Background: Cell-free DNA (cf-DNA)-based liquid biopsy is emerging as a revolutionary new method in individualized cancer treatment and prognosis monitoring, although detecting early-stage cancers using cf-DNA remains challenging, partially because of the undefined biological background of cf-DNA. Materials and methods: We investigated somatic mutations in the cf-DNA of 259 cancer-free individuals with a median age of 47 years using an endogenous barcoding duplex method with an ultralow base error rate (2 × 10 −7 ) and compared the variant allele frequencies (VAFs) of these mutations between the cf-DNA and the corresponding blood cell DNA. Results: Sixty percent (155/259) of the samples showed at least one nonsynonymous mutation on either of two similar target panels covering 508 and 559 cancer-related genes. For individuals older than 50 years of age, the positive rate increased to 76%. Most cf-DNA mutations were also present at similar VAFs in the paired blood cell DNA. The most frequently mutated genes were driver genes of hematologic malignancies, including DNMT3A, TET2, AXSL1, and JAK2 . However, the other 58.4% (192/329) of the mutations were likely 'passenger mutations' of clonal hematopoiesis, including mutations in NOTCH2, FAT3, EXT2, ERBB4, and ARID2, which are driver genes of solid tumors. Conclusion: Hematopoietic clone-derived mutations, including 'driver mutations' and 'passenger mutations', are prevalent in the cf-DNA of both healthy individuals and cancer patients and may be a potential source of false positives in the liquid biopsy. Our results also suggest the ineffectiveness for distinguishing clonal hematopoietic mutations of low VAF (≤0.1%) from tumor-derived mutations using conventional next-generation sequencing of blood cell DNA. However, an error correction model with an ultralow error rate and high coverage depth is required for blood cell DNA sequencing, which is difficult and costly to achieve with current technologies.