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Allogeneic cancer vaccines are designed to induce antitumor immune responses with the goal of impacting tumor growth. Typical allogeneic cancer vaccines are produced by expansion of established cancer cell lines, transfection with vectors encoding immunostimulatory cytokines, and lethal irradiation. More than 100 clinical trials have investigated the clinical benefit of allogeneic cancer vaccines in various cancer types. Results show limited therapeutic benefit in clinical trials and currently there are no FDA-approved allogeneic cancer vaccines. We used recently developed bioinformatics tools including the pVACseq suite of software tools to analyze DNA/RNA-sequencing data from the The Cancer Genome Atlas to examine the repertoire of antigens presented by a typical allogeneic cancer vaccine, and to simulate allogeneic cancer vaccine clinical trials. Specifically, for each simulated clinical trial, we modeled the repertoire of antigens presented by allogeneic cancer vaccines consisting of three hypothetical cancer cell lines to 30 patients with the same cancer type. Simulations were repeated ten times for each cancer type. Each tumor sample in the vaccine and the vaccine recipient was subjected to human leukocyte antigen (HLA) typing, differential expression analyses for tumor-associated antigens (TAA), germline variant calling, and neoantigen prediction. These analyses provided a robust, quantitative comparison between potentially beneficial TAAs and neoantigens versus distracting antigens present in the allogeneic cancer vaccines. We observe that distracting antigens greatly outnumber shared TAAs and neoantigens, providing one potential explanation for the lack of observed responses to allogeneic cancer vaccines. This analysis provides additional rationale for the redirection of efforts toward a personalized cancer vaccine approach. Significance: A comprehensive examination of allogeneic cancer vaccine antigen repertoire using large-scale genomics datasets highlights the large number of distracting antigens and argues for more personalized approaches to immunotherapy that leverage recent strategies in tumor antigen identification.

In Silico Epitope Prediction Analyses Highlight the Potential for Distracting Antigen Immunodominance with Allogeneic Cancer Vaccines