Abstract 546: Supporting neoantigen identification for personalized cancer vaccines trough analytical validation of an augmented content enhanced (ACE) transcriptome

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 neoantigen identification typically involves deep sequencing of the tumor exome and transcriptome combined with exome sequencing of the matched normal. As personalized vaccines enter clinical trials, there is a growing need for strong analytical validation of these platforms. To address this, we have developed the ACE Exome (~200X) and Transcriptome platforms for neoantigen identification which harness an augmented exome approach specifically designed to increase sensitivity for neoantigens in low complexity, difficult to sequence 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, variant types that are especially important for neoantigen identification. In this abstract, we describe the ACE Transcriptome validation. To validate the ACE Transcriptome, we assessed analytical sensitivity (AS) and positive predictive accuracy (PPA). AS was calculated from a reference set of 894 SNVs and 19 indels across 11 tumor cancer cell lines with matched normals. The reference set was constructed based on previously observed variants in CCLE, COSMIC or had been validated by Sanger sequencing. PPA and limit of detection (LOD) were calculated in a series of tumor-normal dilutions representing allele frequencies ranging from 10% to 100%. For fusions, we further selected an additional 10 cell lines to test the detection of 16 previously characterized fusion events including several clinically actionable fusions (ALK and BCR-ABL1). We report an analytical sensitivity for SNVs of >99%, and a PPA of >96% for small variants with >=10% minor allele frequency (MAF). For fusion events, we report an analytical sensitivity of >99%, with the detection of all 16 fusion events supported by at least 5 reads. We demonstrate that our ACE Transcriptome assay and RNA cancer pipeline is a highly sensitive and robust platform for detecting small variants and fusions in the RNA. Citation Format: Jennifer L. Yen, Sean Boyle, Ravi Alla, Jason Harris, Martina Lefterova, Richard Chen. Supporting neoantigen identification for personalized cancer vaccines trough analytical validation of an augmented content enhanced (ACE) transcriptome [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 546. doi:10.1158/1538-7445.AM2017-546