Abstract 396: Detecting copy number variations using WES datasets in patient derived xenografts
Authors: Jia Xue, Wubin Qian, Sheng Guo, Jie Cai, Henry Qixiang Li
Journal: Cancer Research (2017)

Abstract Amplification or deletion of oncogenes and tumor suppressors can be oncogenic, which may serve as drug targets and biomarkers for disease prognosis and drug response. Traditionally, array-based assays, e.g. Affymetrix SNP6.0® and lately OncoScan®, have been used to detect DNA copy number variations (CNVs). These assays have limitations including insufficient probe availability for some genes, cross hybridization, imprecise measurement of fluorescent signal, less suitability of tumor samples, and high cost. Whole exome sequencing (WES) is now widely used to profile tumor samples, and provides a fast and efficient determination for point mutations, insertions and deletions at the DNA level. Recently, WES is being used to profile CNVs with some success, but also suffers from many drawbacks, such as the requirement of paired normal tissues, the need of a large batch of samples, the inadequacy of detecting chromosomal-level CNVs, the incapability to detect CNVs in low coverage genomic regions. We have developed a CNV detection pipeline on both genomic (segment) level and gene level from WES data using the concept of off-target and on-target reads1, and evaluated it in a set of 155 patient-derived xenografts (PDXs), with head-to-head comparison to Affymetrix SNP6.0® and OncoScan® on 5 models with 2 passages from each. PDX is a well-accepted experimental model mimicking original patient in histo- &amp; molecular pathology2. Reads derived from mouse contaminants were removed from WES datasets to avoid mouse signal interference, which cannot be done in array-based techniques. RT-PCR was used to experimentally validate CNVs for selected genes. We found that the average off-target rate for our PDX models is approximately 15%, and off-target reads were uniformly distributed across genome. The comparison with array-based technologies indicates that 1) WES has the highest resolution (20kb) while OncoScan® is the second (50-100 kb), followed by SNP6.0® (100-200kb), 2) the OncoScan® CNV calls are very similar to our WES methods at the genome level, yet 3) WES gives a much higher accuracy on CNV inference for genes flanking the ~900 cancer related genes with enhanced probe densities on OncoScan®, suggesting that our WES method has the highest accuracy. RT-PCR results confirmed the observations. In summary, our WES-based approach gives a better solution in CNV detection in PDX models. References 1. Kuilman T, Velds A, Kemper K, Ranzani M, Bombardelli L, Hoogstraat M, Nevedomskaya E, Xu G, de Ruiter J, Lolkema MP, Ylstra B, Jonkers J, et al. CopywriteR: DNA copy number detection from off-target sequence data. Genome biology 2015;16:49. 2. Guo S, Qian W, Cai J, Zhang L, Wery JP, Li QX. Molecular Pathology of Patient Tumors, Patient-Derived Xenografts, and Cancer Cell Lines. Cancer research 2016;76:4619-26. Citation Format: Jia Xue, Wubin Qian, Sheng Guo, Jie Cai, Henry Qixiang Li. Detecting copy number variations using WES datasets in patient derived xenografts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 396. doi:10.1158/1538-7445.AM2017-396