IMMU-49. PREDICTING PATIENT-SPECIFIC ANTIGENS FOR MEDULLOBLASTOMA IMMUNOTHERAPY

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.

IMMU-24. A COMPREHENSIVE IN SILICO APPROACH TO DISCOVERING TUMOR REJECTION ANTIGENS IN MALIGNANT BRAIN TUMORS

BACKGROUND Proteins that can serve as effective tumor rejection antigens within brain tumors are poorly characterized. Current prediction algorithms relying on identification of mutated epitopes as neoantigens are limited in low mutational burden tumors. We have developed a multi-faceted computer algorighm for identifying tumor rejection antigens called the Open Reading Frame Antigen Network (ORAN). This pipeline provides a comprehensive solution for predicting brain tumor immunogenic targetable epitopes/transcripts. METHODS Human and murine RNASeq and WES data were QCed. Patients individual HLA-I and HLA-II haplotypes were predicted by highly customized Optitype and Phlat Algorithms. SNPs and Indels were called from tumors WES and read through matched RNASeq data. 19,131 transcripts expression were counted per TOIL algorithm. Tumor associate antigens(TAA) of individual patient or murine tumors were selected by setting a cutoff of Transcripts Per Million (TPM) > 1 on individual patient’s tumor, while RNA Seq data from 7000 normal tissues was used to identify tumor unique transcripts. Actual sequence of HLA and SNPed Consenses TAA(CTAA) were called. Only expressed mutations and personalized TAAs were used for antigenic epitope predictions. All neoepitopes were screened against a human reference proteomic library to guarantee that epitopes were not shared by other expressed isoforms or genes. In silico validation were preformed to cross validate predictions made by ORAN. RESULTS In medullobastoma (N=121 samples), ORAN gives an average of 15.6 MHC class I restricted neoepitopes,11.9 epitopes encoded by Indels and with 33.2 MHC class II restricted neoepitopes and 16.2 Indel antigens per patient. The TAAs of each patient reaches average 256 antigenic epitopes per patient. ORAN also predicts the exact HLA and neo-antigens from a validated neoantigens vaccine dataset (Gros A Nat Med 2016). CONCLUSION ORAN accurately identifies validated neoantigens and provides a comprehensive list of potential tumor rejection antigens within human and murine brain tumors.