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Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial.

ABSTRACT: 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.

SUBMITTER: Rubinsteyn A

PROVIDER: S-EPMC5778604 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial.

Rubinsteyn Alex A Kodysh Julia J Hodes Isaac I Mondet Sebastien S Aksoy Bulent Arman BA Finnigan John P JP Bhardwaj Nina N Hammerbacher Jeffrey J

Frontiers in immunology 20180118

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 mo ...[more]

PMID: 29403468

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Project description:Stage III–IV non-small cell lung cancer (NSCLC) is a devastating disease characterized by a poor prognosis. NSCLC tumors carry genetic mutations, which can lead to the expression of altered protein sequences. Peptides originating from mutated proteins and bound to MHC molecules on the tumor cell surface are referred to as neoantigens, as they are tumor-specific and not expressed in normal cells. Due to their tumor specificity, neoantigens have a strong potential to induce an anti-tumor immune response and have been investigated for development of personalized therapeutic cancer vaccines. The current study describes the development of a clinical grade neoantigen vaccine formulation (FRAME-001) intended as immunotherapy in advanced NSCLC in combination with the immune checkpoint inhibitor pembrolizumab. The detection of aberrant tumor-specific transcripts as well as an algorithm to select immunogenic neoantigen peptides are described. Subsequently, selected neoantigen peptides were synthesized with a high throughput synthesis platform and aseptically formulated under good manufacturing practice (GMP) conditions into four aqueous peptides mixtures that each contained six neoantigen peptides. A validated stability-indicating analytical method was developed in which we considered the personalized nature of the formulation. An extensive stability study performed either at −25 °C or −80 °C showed that the formulation was stable for up to 32 weeks. The formulation was mixed with the vaccine adjuvant Montanide ISA 51 VG, which yielded the final vaccine emulsion. The stability of the vaccine emulsion was demonstrated using microscopic examination, differential light scattering, and the water-drop test. The presented data show that FRAME-001 is a feasible personalized vaccine formulation for the treatment of stage III–IV NSCLC. The presented data may give guidance in the development of novel personalized therapeutic vaccines since this formulation strategy could be used for any cancer indication.

Project description:BackgroundReverse vaccinology accelerates the discovery of potential vaccine candidates (PVCs) prior to experimental validation. Current programs typically use one bacterial proteome to identify PVCs through a filtering architecture using feature prediction programs or a machine learning approach. Filtering approaches may eliminate potential antigens based on limitations in the accuracy of prediction tools used. Machine learning approaches are heavily dependent on the selection of training datasets with experimentally validated antigens (positive control) and non-protective-antigens (negative control). The use of one or few bacterial proteomes does not assess PVC conservation among strains, an important feature of vaccine antigens.ResultsWe present ReVac, which implements both a panoply of feature prediction programs without filtering out proteins, and scoring of candidates based on predictions made on curated positive and negative control PVCs datasets. ReVac surveys several genomes assessing protein conservation, as well as DNA and protein repeats, which may result in variable expression of PVCs. ReVac's orthologous clustering of conserved genes, identifies core and dispensable genome components. This is useful for determining the degree of conservation of PVCs among the population of isolates for a given pathogen. Potential vaccine candidates are then prioritized based on conservation and overall feature-based scoring. We present the application of ReVac, applied to 69 Moraxella catarrhalis and 270 non-typeable Haemophilus influenzae genomes, prioritizing 64 and 29 proteins as PVCs, respectively.ConclusionReVac's use of a scoring scheme ranks PVCs for subsequent experimental testing. It employs a redundancy-based approach in its predictions of features using several prediction tools. The protein's features are collated, and each protein is ranked based on the scoring scheme. Multi-genome analyses performed in ReVac allow for a comprehensive overview of PVCs from a pan-genome perspective, as an essential pre-requisite for any bacterial subunit vaccine design. ReVac prioritized PVCs of two human respiratory pathogens, identifying both novel and previously validated PVCs.

Dataset Information

Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial.

Publications

Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial.

Similar Datasets

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