循环肿瘤DNA
计算生物学
生物信息学
数字聚合酶链反应
基因分型
DNA测序
DNA
胎儿游离DNA
液体活检
生物
癌症
基因
聚合酶链反应
遗传学
基因型
胎儿
产前诊断
怀孕
作者
Aaron M. Newman,Alexander F. Lovejoy,Daniel M. Klass,David M. Kurtz,Jacob J. Chabon,Florian Scherer,Henning Stehr,Chih Long Liu,Scott V. Bratman,Carmen Say,Li Zhou,Justin N. Carter,Robert B. West,George W. Sledge,Joseph B. Shrager,Billy W. Loo,Joel W. Neal,Heather A. Wakelee,Maximilian Diehn,Ash A. Alizadeh
摘要
High-throughput sequencing of circulating tumor DNA (ctDNA) promises to facilitate personalized cancer therapy. However, low quantities of cell-free DNA (cfDNA) in the blood and sequencing artifacts currently limit analytical sensitivity. To overcome these limitations, we introduce an approach for integrated digital error suppression (iDES). Our method combines in silico elimination of highly stereotypical background artifacts with a molecular barcoding strategy for the efficient recovery of cfDNA molecules. Individually, these two methods each improve the sensitivity of cancer personalized profiling by deep sequencing (CAPP-Seq) by about threefold, and synergize when combined to yield ∼15-fold improvements. As a result, iDES-enhanced CAPP-Seq facilitates noninvasive variant detection across hundreds of kilobases. Applied to non-small cell lung cancer (NSCLC) patients, our method enabled biopsy-free profiling of EGFR kinase domain mutations with 92% sensitivity and >99.99% specificity at the variant level, and with 90% sensitivity and 96% specificity at the patient level. In addition, our approach allowed monitoring of NSCLC ctDNA down to 4 in 10(5) cfDNA molecules. We anticipate that iDES will aid the noninvasive genotyping and detection of ctDNA in research and clinical settings.
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