Aaron M. Newman 1 , 2 , Alexander F. Lovejoy 1 , 3 , 4 , Daniel M. Klass 1 , 2 , 4 , David M. Kurtz 1 , 2 , 5 , Jacob J. Chabon 1 , Florian Scherer 2 , Henning Stehr 4 , Chih Long Liu 1 , 2 , Scott V. Bratman 1 , 3 , Carmen Say 3 , Li Zhou 4 , Justin N. Carter 3 , Robert B. West 6 , George W. Sledge 2 , 4 , Joseph B. Shrager 7 , Billy W. Loo Jr. 3 , Joel W. Neal 2 , Heather A. Wakelee 2 , Maximilian Diehn 1 , 2 , 3 , Ash A. Alizadeh 1 , 2 , 4 , 8
28 March 2016
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 ~3 fold, 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 clinical non-small cell lung cancer (NSCLC) samples, our method enabled biopsy-free profiling of EGFR kinase domain mutations with 92% sensitivity and 96% specificity and detection of 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.