IMMU-49. PREDICTING PATIENT-SPECIFIC ANTIGENS FOR MEDULLOBLASTOMA IMMUNOTHERAPY. (9th November 2020)

• Record Type: Journal Article
• Authors: Dyson, Kyle; Yang, Changlin; Trivedi, Vrunda; Wildes, Tyler; Grippin, Adam; Mitchell, Duane
• Is Part Of: Neuro-oncology. Volume 22(2020)Supplement 2
• Page Start: ii115
• Page End: ii115
• Publication Date: 2020-11-09
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noaa215.479
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: BACKGROUND: Identification of tumor rejection antigens remains a major barrier to the development of effective immunotherapeutics and their application to pediatric brain tumors. To identify candidate antigen targets for medulloblastoma adoptive cellular therapy, we performed a comprehensive immunogenomic analysis of medulloblastoma transcription profiles and in silico antigen prediction across a broad array of antigen classes. We hypothesized that medulloblastomas are immunologically heterogeneous and express genes with limited normal tissue expression that may serve as targets for immunotherapy. METHODS: Immunologic heterogeneity was assessed using several published algorithms and approaches implemented within the R programming language. Patient-specific HLA haplotypes were called via customized Optitype and Phlat algorithms. Patient-specific tumor associated antigens (TAA) were selected only if expressed >1 transcript per million (TPM) in tumor and the standardized expression across a human tissue database was below 1 TPM. Patient-specific HLA and TAA sequences were extracted from RNA-seq data for prediction with eight MHC class I and four MHC class II affinity algorithms. Only expressed mutations and personalized TAAs were used for antigenic epitope predictions. All epitopes were screened against a human reference proteomic library to guarantee that epitopes were not shared by other expressed isoforms or genes. Public mass-spec data was also screened for protein-level antigen expression. RESULTS: Although absolute immune cell content is predicted to be low, immune gene-signature analysis revealed subgroup-specific differences. Antigen prediction analysis revealed most patients express few candidate neoantigen targets passing all filtering criteria. Importantly, cancer testis antigens as well as previously unappreciated neurodevelopmental antigens were found expressed across all medulloblastoma subgroups and most patients. Protein level antigen expression was confirmed for some predicted TAAs. CONCLUSION: Medulloblastomas are immunologically cold yet subgroups have distinct immune cell gene-signatures. Using a custom antigen prediction pipeline, we identified potential tumor rejection antigens with important implications for development of medulloblastoma cellular therapies.