11. Developing a robust bioinformatics workflow to support personalized neoantigen vaccine clinical trials

Abstract

Personalized cancer vaccines (PCVs) leverage immunogenomics strategies to combat cancer. Somatic mutations in tumor cells generate neoantigens that may get presented on the tumor cell's surface by MHC molecules. Immunotherapies target neoantigens to stimulate tumor-specific immune responses. Our bioinformatics workflow has designed vaccines for over 170 patients across 11 of the 180 neoantigen vaccine trials on clinicaltrials.gov.

Despite the rise in PCV-related interventions, gaps in established protocols addressing the complexities associated with the design of PCVs still remain. Here, we summarize our bioinformatics pipeline and describe measures taken to ensure robust support for clinical trials at Washington University. Our Google Cloud immunotherapy pipeline (open MIT license) to predict neoantigen epitopes is implemented in Workflow Definition Language and containerized using Docker to ensure portability and reliability. The pVACtools software suite (pvactools.org) that carries out neoantigen identification and prioritization, is developed and updated following industry best practices including version control (Git), formal code review, automated unit and integration tests, and benchmark tests. The final steps of the bioinformatics workflow generate files recording the analysis parameters and QC results tailored to the FDA's requests. Candidates generated by the pipeline are reviewed at an Immunogenomics Tumor Board using the pVACview tool. Prioritized candidates undergo a rigorous examination of data QC metrics, variant support at genomic and transcriptomic levels, MHC binding prediction algorithms, and HLA allele concordance between the clinical data and in-silico prediction tools. Finally, a long-peptide order form generated by the pipeline is sent to the vaccine manufacturer for synthesis.