Abstract 428: Supporting neoantigen identification for personalized cancer vaccines through analytical validation of an augmented content enhanced (ACE) exome

Abstract

Abstract The identification of neoantigens has become a critical step in the development of neoantigen-based personalized cancer vaccines and other immunotherapy applications. Since neoantigens can be generated from tumor specific mutations in any expressed gene, the first step in identification of neoantigens typically involves deep exome and transcriptome sequencing on the tumor and exome sequencing of the matched normal. As personalized vaccines enter clinical trials with the potential for clinical use, there is a growing need for strong analytical validation of these platforms. To address this we have developed our ACE Exome (~200X) and Transcriptome platforms for neoantigen identification which utilitize an augmented exome approach designed to increase sensitivity for neoantigens in low complexity, traditionally hard to sequencing regions. To enable this platform for neoantigen based personalized cancer vaccines, we have performed a validation of both our ACE Exome (tumor and normal) and ACE transcriptome platforms for detecting DNA-based SNVs and Indels, as well as for RNA based small variant and fusion calls. These are variant types are especially important for neoantigen identification. In this abstract we describe the ACE Exome validation. We used 11 cancer cell lines and their matched normals to assess analytical sensitivity and limits of detection (LOD) for small variant (SNV and Indel) detection using our ACE exome and Tumor Normal bioinformatics pipeline. We identified a gold set of variants, 875 SNVs and 19 Indels that were previously validated in these 11 cell lines (COSMIC, CCLE and Sanger Sequencing confirmed variants). These gold set variants were used to calculate our analytical sensitivity (percent of gold variants detected across the 11 cell line pairs using our assay). To determine our LOD, we chose 3 of the 11 cancer cell lines and created 6 dilutions (5%, 10%, 20%, 30%, 50% and 80% tumor purity) with their matched normal. We then determined Positive Predictive Agreement (PPA, percent of pure cell line variants detected in a diluted samples) and False Discovery Rate (FDR, percent of erroneously detected variants in the diluted sample that were not detected in the pure cell lines) metrics for variants across different minor allelic frequencies (MAF) in the diluted samples. The ACE “Tumor Normal” Exome assay had a high sensitivity of 98% for SNVs and 95% for Indels. The assay also showed robust PPA (sensitivity) of 97% and FDR (specificity) of 2% for SNVs with MAF >= 10% and PPA of 87% and FDR of 3% for Indels with MAF >= 10%. We demonstrate that the ACE “Tumor Normal” Exome assay is highly accurate for identification of SNVs and Indels in cancer exomes. With high analytical sensitivity, PPA and low FDR we believe this assay provides augmented ability to detect cancer driver and potential neoantigen generating mutations across various tumor types. Citation Format: Ravi K. Alla, Jennifer Yen, Sean M. Boyle, Richard Chen. Supporting neoantigen identification for personalized cancer vaccines through analytical validation of an augmented content enhanced (ACE) exome [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 428. doi:10.1158/1538-7445.AM2017-428