Ultra-Sensitive Mutation Detection and Genome-Wide DNA Copy Number Reconstruction by Error-Corrected Circulating Tumor DNA Sequencing

利用纠错循环肿瘤DNA测序进行超灵敏突变检测和全基因组DNA拷贝数重建

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Abstract

BACKGROUND: Circulating free DNA sequencing (cfDNA-Seq) can portray cancer genome landscapes, but highly sensitive and specific technologies are necessary to accurately detect mutations with often low variant frequencies. METHODS: We developed a customizable hybrid-capture cfDNA-Seq technology using off-the-shelf molecular barcodes and a novel duplex DNA molecule identification tool for enhanced error correction. RESULTS: Modeling based on cfDNA yields from 58 patients showed that this technology, requiring 25 ng of cfDNA, could be applied to >95% of patients with metastatic colorectal cancer (mCRC). cfDNA-Seq of a 32-gene, 163.3-kbp target region detected 100% of single-nucleotide variants, with 0.15% variant frequency in spike-in experiments. Molecular barcode error correction reduced false-positive mutation calls by 97.5%. In 28 consecutively analyzed patients with mCRC, 80 out of 91 mutations previously detected by tumor tissue sequencing were called in the cfDNA. Call rates were similar for point mutations and indels. cfDNA-Seq identified typical mCRC driver mutations in patients in whom biopsy sequencing had failed or did not include key mCRC driver genes. Mutations only called in cfDNA but undetectable in matched biopsies included a subclonal resistance driver mutation to anti-EGFR antibodies in KRAS, parallel evolution of multiple PIK3CA mutations in 2 cases, and TP53 mutations originating from clonal hematopoiesis. Furthermore, cfDNA-Seq off-target read analysis allowed simultaneous genome-wide copy number profile reconstruction in 20 of 28 cases. Copy number profiles were validated by low-coverage whole-genome sequencing. CONCLUSIONS: This error-corrected, ultradeep cfDNA-Seq technology with a customizable target region and publicly available bioinformatics tools enables broad insights into cancer genomes and evolution. CLINICALTRIALSGOV IDENTIFIER: NCT02112357.

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