While methods for detecting SNVs and indels in circulating tumor DNA (ctDNA) with hybridization capture-based next-generation sequencing (NGS) have been available, copy number variations (CNVs) detection is more challenging. Here, we present a method enabling CNV detection from a 150-gene panel using a very low amount of ctDNA. First, a read depth-based CNV estimation method without a paired blood sample was developed and cfDNA sequencing data from healthy people were used to build a panel of normal (PoN) model. Then, in silico and in vitro simulations were performed to define the limit of detection (LOD) for EGFR, ERBB2, and MET. Compared to the WES results of the 48 samples, the concordance rate for EGFR, ERBB2, and MET CNVs was 78%, 89.6%, and 92.4%, respectively. In another cohort profiled with the 150-gene panel from 5980 lung cancer ctDNA samples, we detected the three genes' amplification with comparable population frequency with other cohorts. One lung adenocarcinoma patient with MET amplification detected by our method reached partial response to crizotinib. These findings show that our ctDNA CNV detection pipeline can detect CNVs with high specificity and concordance, which enables CNV calling in a non-invasive way for cancer patients when tissues are not available.
Keywords: circulating tumor DNA; copy number variations; non-small cell lung cancer; targeted sequencing.