Abstract 6580: A machine learning-based pipeline and web server ImmuneMirror for neoantigen prediction

Abstract Background: Gastrointestinal tract (GIT) cancer is a group of cancers that affect the digestive system. It has become a leading cause of cancer mortality worldwide. However, the clinical outcome for advanced-stage patients with cancer metastasis after conventional treatment remains poor. Although cancer immunotherapy has attracted much attention for the treatment of these deadly cancers as a single agent or in combination with chemotherapy, the objective response rate to this treatment remains low and most of the responsive patients only have a partial response. Accumulating evidence suggests that multiple mechanisms are involved in the resistance to cancer immunotherapy. Methods: To facilitate the identification of the cancer patients who most likely respond to immunotherapy, we developed a stand-alone open-source ImmuneMirror pipeline with graphical analysis results for evaluating the selected features including tumor mutation burden, microsatellite instability (MSI) status, HLA typing, predicted neoantigen load and the top-ranked neoantigens with T cell immunogenicity as well as the expression of innate anti-PD1 resistance (IPRES) gene signature. We incorporated a machine-learning model for neoantigen prediction and prioritization in ImmuneMirror and established a web server. The model was trained and tested using the known neopeptides with T cell immunogenicity collected from 19 published studies, and combined neoantigen-relevant features: agretopicity, foreignness, hydrophobicity, binding stability, peptide processing, and transportation scores. Results: The accuracy of neoantigen prediction accuracy of ImmuneMirror model is 0.87. We utilized ImmuneMirror to analyze 805 normal-tumor paired samples in GIT cancers including esophageal squamous cell carcinoma (ESCC), colorectal cancer (CRC) and hepatocellular carcinoma (HCC). Elevated neoantigen load was correlated with good clinical outcomes in ESCC. Interestingly, we identified a subgroup of MSI-H CRC with relatively low neoantigen loads for MHC Class I and Class II, respectively, though the tumor mutation burdens of this subset of patients were comparable with other MSI-H CRC. Our result suggests this subset of patients may not respond well to immunotherapy. In addition, the neopeptide YMCNSSCMGV derived from the TP53 hotpot mutation G245V mutation restricted by HLA-A02 was identified as the actional target in the ESCC patient by ImmuneMirror and confirmed by the experimental validation. Conclusions: These results showed the reliability and effectiveness of ImmuneMirror for evaluating the relevant features and predicting putative neoantigens. Acknowledgments: We acknowledge the support from Health Medical Research Fund (Grant No. 07182016) from the Research Fund Secretariat of Health Bureau in Hong Kong. Citation Format: Wei Dai, Gulam Sarwar Chuwdhury, Yunshan Guo, Zhonghua Liu. A machine learning-based pipeline and web server ImmuneMirror for neoantigen prediction. [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 6580.