Biology subjects

Rubinsteyn, A.

Publications and source records attributed to Rubinsteyn, A..

4 recordsLinked to original sources

bioRxiv2017

Computational pipeline for the PGV-001 neoantigen vaccine trial

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 patients 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.

bioinformatics↗

bioRxiv2017

MHCflurry: open-source class I MHC binding affinity prediction

Machine learning prediction of the interaction between major histocompatibility complex I (MHC I) proteins and their small peptide ligands is important for vaccine design and other applications in adaptive immunity. We describe and benchmark a new open-source MHC I binding prediction package, MHCflurry. The software is a collection of allele-specific binding predictors incorporating a novel neural network architecture and adhering to software development best practices. MHCflurry outperformed the standard predictors NetMHC 4.0 and NetMHCpan 3.0 on a benchmark of mass spec-identified MHC ligands and showed competitive accuracy on a benchmark of affinity measurements. The accuracy improvement was due to substantially better prediction of non-9-mer peptide ligands, which offset a narrowly lower accuracy on 9-mers. MHCflurry was on average 8.6X faster than NetMHC and 44X faster than NetMHCpan; performance is further increased when a graphics processing unit (GPU) is available. MHCflurry is freely available to use, retrain, or extend, includes Python library and command line interfaces, and may be installed using standard package managers.

immunology↗

bioRxiv2017

Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines

Therapeutic vaccines targeting mutant tumor antigens (\"neoantigens\") are an increasingly popular form of personalized cancer immunotherapy. Vaxrank is a computational tool for selecting neoantigen vaccine peptides from tumor mutations, tumor RNA data, and patient HLA type. Vaxrank is freely available at www.github.com/openvax/vaxrank under the Apache 2.0 open source license and can also be installed from the Python Package Index.

bioinformatics↗

bioRxiv2016

Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade

Immune checkpoint inhibitors are promising treatments for patients with a variety of malignancies. Toward understanding the determinants of response to immune checkpoint inhibitors, it was previously demonstrated that somatic mutation burden is associated with benefit and a hypothesis was posited that neoantigen homology to pathogens may in part explain the link between somatic mutations and response. To further examine this hypothesis, we reanalyzed cancer exome data obtained from a previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of those patients. We found that the predictive accuracy does not increase as analysis narrows from somatic mutation burden to predicted MHC Class I neoantigens, expressed neoantigens, or homology to pathogens. Further, the association between somatic mutation burden and response is only found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden are also associated with response, but neither is more predictive than somatic mutation burden. Neither the previously-described tetrapeptide signature nor an updated method to evaluate neoepitope homology to pathogens were more predictive than mutation burden.

cancer biology↗