Abstract
Reliable detection of somatic copy-number alterations (sCNAs) in tumors using whole-exome sequencing (WES) remains challenging owing to technical (inherent noise) and sample-associated variability in WES data. We present a novel computational framework, ENVE, which models inherent noise in any WES dataset, enabling robust detection of sCNAs across WES platforms. ENVE achieved high concordance with orthogonal sCNA assessments across two colorectal cancer (CRC) WES datasets, and consistently outperformed a best-in-class algorithm, Control-FREEC. We subsequently used ENVE to characterize global sCNA landscapes in African American CRCs, identifying genomic aberrations potentially associated with CRC pathogenesis in this population. ENVE is downloadable at https://github.com/ENVE-Tools/ENVE.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0192-9) contains supplementary material, which is available to authorized users.
Highlights
Human cancer is caused in part by structural changes resulting in DNA copy-number alterations at distinct locations in the tumor genome
We demonstrate the robustness of ENVE’s performance in two independent matched tumor/normal whole-exome sequencing (WES) datasets, derived from Caucasian and African American (AA) colorectal cancers (CRC), by comparing ENVE-based somatic copy-number alterations (sCNA) calls in WES data against single nucleotide polymorphism (SNP) arrays and quantitative real-time PCR-based sCNA assessments performed on the same sample sets
We describe the key computational steps in the ENVE methodology and evaluate its performance using two matched tumor/normal WES datasets, an in-house WES dataset of predominantly late-stage, microsatellite stable (MSS) AA CRCs (N = 30) [20], and a Caucasian MSS CRC WES
Summary
Human cancer is caused in part by structural changes resulting in DNA copy-number alterations at distinct locations in the tumor genome. Identification of such somatic copy-number alterations (sCNA) in tumor tissues has contributed significantly to our understanding of the pathogenesis and to the expansion of therapeutic avenues across multiple cancers [1,2,3,4]. We demonstrate the robustness of ENVE’s performance in two independent matched tumor/normal WES datasets (total N = 107), derived from Caucasian and African American (AA) colorectal cancers (CRC), by comparing ENVE-based sCNA calls in WES data against SNP arrays and quantitative real-time PCR (qPCR)-based sCNA assessments performed on the same sample sets. Using the ENVE framework, we characterize, for the first time, global sCNA landscapes in colon cancers arising in AA patients, identifying genomic aberrations potentially associated with colon carcinogenesis in this population
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
