GSE136619
Whole exome and transcriptome sequencing of murine tumor models
These data were used for prediction of neoantigens and minor histocompatibility mismatch antigens, which were subsequently used for design of a machine-learning algorithm for tumor-specific antigen (TSA) immunogenicity prediction.  TSA vaccines are a growing area of study for cancer immunotherapy, but identification of clinically relevant targets remains a challenge. The study associated with these data provides the first description of a computational method for direct prediction of TSA immunogenicity trained entirely from validated TSA immunogenicity scores. This tool has allowed us to 1) predict for clinically efficacious TSA targets, 2) identify genomic correlates of TSA immunogenicity, and 3) demonstrate evidence of alternative out-of-frame TSAs which can promote anti-tumor immunity. Overall design: Examination of whole exome and transcriptome patterns in 6 murine tumor models, as well as whole exome patterns in respective wild-type cells. Quantified processed data are not available for the RNA-seq data because gene expression data were not generated or considered for the analysis.