Abstract 238: Exhaustive tumor specific antigen detection with RNAseq

Background: Tumor specific antigen (TSA) identification in human cancer predicts response to immunotherapy and provides vaccine targets for precision medicine. In addition to neoantigens from somatic coding mutations, numerous non-mutated TSAs can elicit T-cell responses but are often overlooked by current methods. We present a method that accurately and comprehensively predict TSAs from RNAseq data regardless of mutation status. Methods: HLA-I genotypes were predicted with seq2HLA. RNAseq fastq files were translated into all possible peptides of length 8-11, and peptides with high expression in the tumor and comparatively low expression in normal were tested for their MHC-I binding potential with netMHCpan-4.0. We defined our predicted TSA by i) high expression in tumor samples, ii) low expression in normal samples, and iii) high predicted patient-specific MHC-I binding affinity. Results: We developed a novel pipeline for TSA prediction from RNAseq that is not limited to mutation-derived TSAs. This pipeline was used to predict all possible unique peptides size 8-11 on previously published murine and human lung and lymphoma tumors then validated on matched tumor and control lung adenocarcinoma (LUAD) samples. This pipeline is able to predict TSAs in MHC-I ligand-purified proteomics data with favorable performance to existing methods. Furthermore, neoantigens predicted by exomeSeq are typically poorly expressed at the RNA level, (28% of predicted neoantigens with >0 expression, mean of 15.6 reads/sample) and a fraction of them (47/6,928, 0.68%) are expressed in matched normal samples. Finally, a set of 6 TSAs are expressed in 22/39 (56%) of LUAD tumors and represent attractive vaccine targets. Conclusion: Direct quantification of RNAseq evidence of the potential peptidome in matched tumor and control RNAseq samples, via our novel pipeline, allows for exhaustive detection of TSAs. Citation Format: Michael Sharpnack, Travis Johnson, Robert Chalkley, Zhi Han, David Carbone, Kun Huang, Kai He. Exhaustive tumor specific antigen detection with RNAseq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 238.