Abstract 2065: A comprehensive online system for identifying tumor neoantigens

Abstract

Abstract Genetic instability of cancer cells often generates abundant somatic mutations, and these non-synonymous variations can produce mutated tumor-specific antigens (mTSAs), usually referred to as neoantigens. In addition to mutation-derived neoantigens, aberrantly expressed TSAs (aeTSAs), arising from epigenetic changes and cis- or trans-acting genetics, are also prospective materials for cancer immunotherapy. Since these neoantigens are highly immunogenic, they can activate T cells to trigger an immune response. So far, nevertheless, none of current neoantigen identification tools can identify and differentiate mTSAs and aeTSAs. Here, we demonstrated a comprehensive pipeline and online system to discover both mTSAs and aeTSAs from DNA-seq, RNA-seq, and liquid chromatography-MS/MS (LC-MS/MS) data. To identify potential mTSAs, somatic mutations are discovered and annotated. Afterward, missense mutations and flanking nucleotide bases are further translated into peptides. Quantification of gene expression is conducted to filter out those peptides with low expression levels if RNA-seq reads are provided as well. To identify aeTSAs, most of which originate from allegedly noncoding regions, an alignment-free approach is employed. The RNA-seq reads from tumor and normal tissues are chopped into short k-mer sequences. Those presenting in tumors but not normal tissues are kept, assembled into longer sequences, and translated into peptides then. LC-MS/MS peptides are optionally provided to improve the confidence of the results. Binding affinities between specific major histocompatibility complexes (MHC) and all translated peptides are predicted. Generally, if a tumor-specific peptide can successfully bind with the MHC on the surfaces of tumor cells, it will be defined as a potential neoantigen candidate. The databases associated with cancer proteomics such as Catalogue of Somatic Mutations in Cancer (COSMIC) database are also included in the system, providing information on common cancer variants. We applied the online system to identify 95 putative aeTSA candidates sharing among 13 patients with colorectal cancer, and on top of that 14 of them could be presented by HLA-A*11:01 & 11:02, which are common alleles in Asians. We also compared 246 putative mTSA candidates with peptides in COSMIC database and found 15 of them were common cancer variants, including KRAS and BRAF mutations, both of which are prognostic and predictive biomarkers in colorectal cancer. More importantly, initial evidence shows that these candidates are immunogenic on primary peripheral blood mononuclear cells. Overall, we propose a user-friendly and practical online system to identify and differentiate mTSAs and aeTSAs with optimized procedures. It integrates analysis results of various inputs, i.e., DNA-seq, RNA-seq, and LC-MS/MS data, which can improve the reliability of identified TSAs and provide valuable information for clinical investigators. Citation Format: Chia-Yu Sung, Chien-Yueh Lee, Chia-Hsin Wu, Kevin Chih-Yang Huang, Yu-Hsuan Tseng, Mong-Hsun Tsai, Liang-Chuan Lai, Tzu-Pin Lu, Kun-San Clifford Chao, Eric Y. Chuang. A comprehensive online system for identifying tumor neoantigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2065.