Abstract 604: Accurate identification and prioritization of candidate neoantigens from integrated cancer exome and transcriptome sequencing of FFPE samples
Authors: Marián Novak, Sam Angiuoli, Luis A. Diaz, Andrew Georgiadis, Sian Jones, Peter R. Loverso, Sonya Parpart-Li, Maria Sevdali, Victor E. Velculescu, Ellen L. Verner, James White, Theresa Zhang, Mark Sausen
Journal: Cancer Research (2017)

Abstract Precise identification and characterization of candidate neoantigens is important for the development of effective cancer vaccines, adoptive T-cell transfer, and prediction of response to checkpoint inhibitors. The candidate tumor neoantigens are actionable only when expressed, however, current prediction methods lack the capacity to evaluate neoantigen expression. Sequencing both DNA and RNA from a patient’s tumor tissue enables identification of mutations and evaluation of their expression leading to accurate identification of putative neoantigens. The purpose of this study was to develop and validate a methodology for co-extraction and sequencing of DNA and RNA from formalin-fixed paraffin-embedded (FFPE) samples to enable a robust neoantigen prediction protocol that integrates whole exome and transcriptome data to identify and prioritize tumor neoantigens for application in immuno-oncology research and clinical trials. In order to prepare high-quality sequencing libraries from FFPE specimens, the tissue was macrodissected to enrich for tumor-specific material, and improve the overall accuracy of next-generation sequencing for detection of somatic alterations. Total DNA and RNA was co-extracted and purified. The DNA was used to prepare whole exome sequencing (WES) libraries, while the co-extracted RNA was ribosome-depleted, and reverse-transcribed to prepare RNA sequencing (RNAseq) libraries. The WES and RNAseq data was then analyzed using a multi-algorithm HLA typing and neoantigen prediction protocol (ImmunoSelect-RTM). ImmunoSelect-R evaluates somatic genomic alterations identified from WES of tumor and matched normal tissue to ensure appropriate prediction of candidate neoantigens. The process of neoantigen prediction was then refined by integration of patient tumor-matched RNAseq data, which allowed for removal of non-expressed putative neoantigens. To further validate the approach, we applied the methodology to a set of experimentally validated neoantigens. In this setting, ImmunoSelect-R correctly classified 18 out of 19 as strong neoantigen candidates, suggesting a sensitivity of greater than 90%. Moreover, in a set of 10 patients, ImmunoSelect-R consistently ranked experimentally validated neoantigens within the top 20% of all neoantigen candidates derived from whole exome sequencing. In summary, our combined tissue processing, macrodissection, co-extraction, and neoantigen prediction methodology is able to identify and prioritize candidate neoantigens. Our approach is unique in combining high-fidelity sequencing (WES) and expression (RNAseq) data to accurately inform the selection of actionable tumor neoantigens for immuno-oncology applications. Citation Format: Marián Novak, Sam Angiuoli, Luis A. Diaz, Andrew Georgiadis, Sian Jones, Peter R. Loverso, Sonya Parpart-Li, Maria Sevdali, Victor E. Velculescu, Ellen L. Verner, James White, Theresa Zhang, Mark Sausen. Accurate identification and prioritization of candidate neoantigens from integrated cancer exome and transcriptome sequencing of FFPE samples [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 604. doi:10.1158/1538-7445.AM2017-604