This paper describes the sequencing protocol and computational pipeline for the PGV-001 personalized vaccine trial. PGV-001 is a therapeutic peptide vaccine targeting neoantigens identified from patient tumor samples. Peptides are selected by a computational pipeline that identifies mutations from tumor/normal exome sequencing and ranks mutant sequences by a combination of predicted Class I MHC affinity and abundance estimated from tumor RNA. The personalized genomic vaccine (PGV) pipeline is modular and consists of independently usable tools and software libraries. We hope that the functionality of these tools may extend beyond the specifics of the PGV-001 trial and enable other research groups in their own neoantigen investigations.
: This paper describes the sequencing protocol and computational pipeline for the PGV-001 personalized vaccine trial. PGV-001 is a therapeutic peptide vaccine targeting neoantigens identified from patient tumor samples. Peptides are selected by a computational pipeline which identifies mutations from tumor/normal exome sequencing and ranks mutant sequences by a combination of predicted Class I MHC affinity and abundance estimated from tumor RNA. The PGV pipeline is modular and consists of many independently usable tools and software libraries. We draw attention to three particular tools which may be useful to other groups working on neoantigen vaccination. (1) Epidisco is a workflow which orchestrates the parallel execution of the PGV pipeline, including both common steps such as alignment as well as tools which have been developed specifically for the PGV-001 trial. (2) Vaxrank uses somatic variants and tumor RNA reads to select peptides for inclusion in a patient's vaccine. (3) Isovar is a library used by Vaxrank to determine the mutated protein sequence associated with a genomic variant from supporting tumor RNA reads. We hope that the functionality of these tools may extend beyond the specifics of the PGV-001 trial and enable other research groups in their own neoantigen investigations.
ReferencesTo conduct our computational experiments, our team developed a set of workflow-management-related projects: Ketrew, Biokepi, and Coclobas. The family of tools and libraries are designed with reliability and flexibility as main guiding principles. We describe the components of the software stack and explain the choices we made. Every piece of software is free and open-source; the umbrella documentation project is available at https://github.com/hammerlab/wobidisco.
PGV-001 (Personalized Genomic Vaccine) is a Phase I clinical trial at Mount Sinai testing the safety, feasibility, and immunogenicity of a neoantigen + Poly-ICLC vaccine derived from exome and RNA sequencing [1]. The personalized component of the vaccine consists of ten peptides (25 amino acids in length) predicted to contain highly expressed mutated cytotoxic T-cell epitopes. Selecting peptides for each patient requires a number of standard bioinformatics tasks such as somatic variant calling, sequence-based HLA typing, and class I peptide-MHC binding prediction, as well as new methods to determine the coding sequence for each identified mutation, quantify the RNA support for each variant allele, and scoring each vaccine peptide. We present the overall design of PGV, its implementation as an open source computational pipeline, and several independently useful libraries that implement aspects of mutant epitope prediction. One of the unique characteristics of the PGV pipeline is that the amino acid sequence of a candidate vaccine peptide is determined from RNA reads overlapping a variant [2]. Consequently, variants which lack any evidence in RNA cannot be used in PGV. Determining a coding sequence from RNA allows us to capture atypical splicing around a mutation as well as phase somatic variants with nearby germline variants. Once a mutant coding sequence is established, candidate vaccine peptides are ranked by combining their predicted Class I MHC binding affinity with allele-specific expression [3]. We demonstrate the impact of different ranking criteria on vaccine efficacy using a tumor mouse model. Execution and dependency management of the PGV pipeline is orchestrated using a a novel workflow engine [4, 5] which allows us to run complex tools across many machines with fault tolerance and reproducibility. The pipeline can be run on single machines, local clusters, or on public infrastructure using the Google Cloud Platform. We hope that by making the PGV pipeline and its underlying components available as open source software we can facilitate faster experimentation in the design of personalized cancer vaccines. [1] https://clinicaltrials.gov/ct2/show/NCT02721043 [2] https://github.com/hammerlab/isovar [3] https://github.com/hammerlab/vaxrank [4] https://github.com/hammerlab/ketrew [5] https://github.com/hammerlab/biokepi Citation Format: Alexander Rubinsteyn, Isaac Hodes, Timothy O'Donnell, Sebastien F. Mondet, John P. Finnigan, Philip Friedlander, Rachel Sabado, Nina Bhardwaj, Jeffrey Hammerbacher. Computational pipeline for a personalized genomic vaccine trial [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr A022.
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