Abstract 5355: PANCeq - Development and validation of a targeted sequencing assay for detection of mutations and copy number changes in most recurrently mutated driver genes
Authors: Meissner Tobias , Amallraja Anu , Young Brandon Michael , Mark Adam , Conolly Cayce , Andrews Amanda , Williams Casey , Leyland-Jones Brian 
Published: July 2017

Next generation sequencing is becoming increasingly prevalent as a tool for the identification of genetic variation to aid diagnosis and support therapy decisions in routine clinical care. It has been shown that tumors are comprised of subpopulations of cells with distinct genomic alterations, and hence tumor heterogeneity is becoming an important factor in deciding treatment. Deep sequencing of tumor samples allows for the determination of number and proportions of low variant allele frequency tumor subclones. Currently, high costs for whole-exome deep sequencing (&gt; 250X) limit its application in routine clinical care, and there is a need for more affordable targeted approaches. Here we present PAN Cancer Sequencing (PANCeq), a targeted deep sequencing panel for the detection of mutations and copy number variants (CNVs) in most recurrently mutated cancer driver genes. A platform-independent computational Docker-based pipeline for data analysis and automatic report generation was developed. PANCeq uses customized targeted enrichment, followed by next-generation sequencing of (FFPE) tumor and matched germline samples.

We included 12 patient samples (breast, ovarian, kidney and prostate) as well as triplicates of 3 reference standards (HD200, HD753, HD733 - Horizon). These were sequenced on Illumina NextSeq o an average depth of 1000X. Sequencing data was aligned using BWA to hg19 human reference. Somatic variants and CNVs were identified using VarDict and CNVkit respectively.

We mined TCGA data across 26 tumor types, and selected known mutations from the most recurrently mutated genes, yielding 2849 mutations across 467 genes. In addition, we included 79 pharmacogenomic relevant and 218 sample tracking SNPs as well as 80 cancer relevant CNVs (whole exon), and designed a custom panel based on their genomic locations, resulting in a panel size of 708kb. To assess performance, we sequenced reference standards with known variants spiked-in at varying VAF (1 - 41.5%). Somatic variant calling was performed against a wildtype sample. For HD200, out of 21 covered mutations, 14 were correctly classified as germline, and 7 as somatic. For HD733, out of 21 mutations, 3 were correctly identified as germline, 18 as somatic. For HD753, all 6 germline and somatic mutations were correctly identified. The 2 CNVs spiked-in were also correctly identified. In all cases we found a good agreement between observed and expected VAF. Somatic variant results for 12 patient samples were compared with variants reported by a private vendor. While we found good agreement at VAF &gt; 20%, our panel detected on average 7 additional clinically relevant mutations at &lt; 20%.

We demonstrate that PANCeq is an accurate and sensitive method for timely identification of clinically actionable mutations and copy number variations across multiple cancer types.

Citation Format: Tobias Meissner, Anu Amallraja, Brandon Michael Young, Adam Mark, Cayce Conolly, Amanda Andrews, Casey Williams, Brian Leyland-Jones. PANCeq - Development and validation of a targeted sequencing assay for detection of mutations and copy number changes in most recurrently mutated driver genes [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 5355. doi:10.1158/1538-7445.AM2017-5355