Abstract 4233: Improving the diagnostic yield of a 124 gene pediatric solid tumor panel through somatic copy number variation analysis
Authors: Raghu Chandramohan, Jacquelyn Reuther, Ilavarasi Gandhi, Horatiu Voicu, Karla R. Alvarez, Kevin E. Fisher, Dolores H. Lopez-Terrada, Donald W. Parsons, Angshumoy Roy
Journal: Cancer Research (2019)

Abstract In cancer genomes, both large-scale CNVs (&amp;gt;100 kb) spanning chromosome arms, and smaller CNVs limited to a few genes are prevalent. While large CNVs are readily detected with existing methodologies (e.g. SNP array), gene-level CNVs require a higher resolution not routinely available in current tools, including next generation sequencing (NGS)-based clinical cancer mutation panels. We sought to leverage NGS data generated by one of these panels: the TCH Pediatric Solid Tumor panel (124 cancer genes), and built a clinical-grade analytic pipeline for detection of somatic CNVs. After reviewing literature, CNVkit was selected for its ability to perform unmatched (requiring no matched normal specimen) CNV analysis, customize segmentation algorithms, and provide superior visualization. CNVkit performs circular binary segmentation on the log2 difference of binned read depths (on- and off-target) from tumor and pooled-normal blood samples to identify CNVs. Although the panel captures only 124 genes (~1Mbp) at &amp;gt; 300X coverage, the low background coverage of 0.05X (off-target) due to imperfect hybridization capture allows us to detect chromosome-arm level changes. As a pilot study, we optimized CNVkit to detect gene-level and chromosome arm-level CNVs in a reference aneuploid colon cancer cell line (HT-29) characterized by aCGH and observed high correlation (r = 0.89) between the aCGH and CNVkit fold change. Through in-silico and bench experiments we performed limit of detection analysis, by diluting different proportions of CNV positive tumor samples with normal sample, to set thresholds for calling amplification and losses. We show the ability of the pipeline to detect shallow and deep deletions if they are present in at least 40% and 80% of the sample sequenced, respectively. During validation, we used 24 pediatric cancer samples with diverse histology to confirm and detect numerous CNVs of clinical significance, including deletions of SMARCB1, PTEN, BRCA2, RB1, and ARID1B, and amplifications of MYCN, MYC, CCND1 and KRAS. As the panel densely tiles tumor suppressors, we are also able to infer apparent intragenic deletions in BRCA1, BRCA2, ATRX, RB1 and PTEN, highlighting the theoretical resolution of this tool for detecting intragenic events over other methods. We have also developed a Python based interactive CNV Viewer for assessing the copy number analysis results from NGS data. The browser like interface allows the user to zoom, link out to UCSC Genome Browser, hover for information, take high-quality screenshots, etc. Hence, the CNV pipeline we have developed will allow more comprehensive evaluation of the existing integrated DNA and RNA analysis pipeline, increasing the diagnostic yield of mutation panel testing for childhood cancer patients. Citation Format: Raghu Chandramohan, Jacquelyn Reuther, Ilavarasi Gandhi, Horatiu Voicu, Karla R. Alvarez, Kevin E. Fisher, Dolores H. Lopez-Terrada, Donald W. Parsons, Angshumoy Roy. Improving the diagnostic yield of a 124 gene pediatric solid tumor panel through somatic copy number variation analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4233.