Predicting the binding affinity of major histocompatibility complex I (MHC I) proteins and their peptide ligands is important for vaccine design. We introduce an open-source package for MHC I binding prediction, MHCflurry. The software implements allele-specific neural networks that use a novel architecture and peptide encoding scheme. When trained on affinity measurements, MHCflurry outperformed the standard predictors NetMHC 4.0 and NetMHCpan 3.0 overall and particularly on non-9-mer peptides in a benchmark of ligands identified by mass spectrometry. The released predictor, MHCflurry 1.2.0, uses mass spectrometry datasets for model selection and showed competitive accuracy with standard tools, including the recently released NetMHCpan 4.0, on a small benchmark of affinity measurements. MHCflurry's prediction speed exceeded 7,000 predictions per second, 396 times faster than NetMHCpan 4.0. MHCflurry is freely available to use, retrain, or extend, includes Python library and command line interfaces, may be installed using package managers, and applies software development best practices.

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 the presence of somatic mutations is associated with benefit from checkpoint inhibition. 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 our previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of these patients. We found that the ability to accurately predict patient benefit did not increase as the analysis narrowed from somatic mutation burden, to inclusion of only those mutations predicted to be MHC class I neoantigens, to only including those neoantigens that were expressed or that had homology to pathogens. The only association between somatic mutation burden and response was found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden were also associated with response, but neither was more predictive than somatic mutation burden. Neither the previously described tetrapeptide signature nor an updated method to evaluate neoepitope homology to pathogens was more predictive than mutation burden. Cancer Immunol Res; 5(1); 84–91. ©2016 AACR.

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.

Background: Early in the pandemic, we designed a SARS-CoV-2 peptide vaccine containing epitope regions optimized for concurrent B cell, CD4⁺T cell, and CD8⁺T cell stimulation. The rationale for this design was to drive both humoral and cellular immunity with high specificity while avoiding undesired effects such as antibody-dependent enhancement (ADE).Methods: We explored the set of computationally predicted SARS-CoV-2 HLA-I and HLA-II ligands, examining protein source, concurrent human/murine coverage, and population coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, sequence conservation, source protein abundance, and coverage of high frequency HLA alleles. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering for surface accessibility, sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites.Results: From 58 initial candidates, three B cell epitope regions were identified. From 3730 (MHC-I) and 5045 (MHC-II) candidate ligands, 292 CD8⁺and 284 CD4⁺T cell epitopes were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we proposed a set of 22 SARS-CoV-2 vaccine peptides for use in subsequent murine studies. We curated a dataset of ~ 1000 observed T cell epitopes from convalescent COVID-19 patients across eight studies, showing 8/15 recurrent epitope regions to overlap with at least one of our candidate peptides. Of the 22 candidate vaccine peptides, 16 (n = 10 T cell epitope optimized; n = 6 B cell epitope optimized) were manually selected to decrease their degree of sequence overlap and then synthesized. The immunogenicity of the synthesized vaccine peptides was validated using ELISpot and ELISA following murine vaccination. Strong T cell responses were observed in 7/10 T cell epitope optimized peptides following vaccination. Humoral responses were deficient, likely due to the unrestricted conformational space inhabited by linear vaccine peptides.Conclusions: Overall, we find our selection process and vaccine formulation to be appropriate for identifying T cell epitopes and eliciting T cell responses against those epitopes. Further studies are needed to optimize prediction and induction of B cell responses, as well as study the protective capacity of predicted T and B cell epitopes.

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.

Immunotherapies like immune checkpoint inhibitors (ICI) have changed the standard of care for patients with cancer, often leading to durable responses. However, many patients remain or become refractory to ICIs owing to factors such as a lack of primed neoantigen-reactive T cells. We developed a peptide-based vaccination platform that utilizes fully personalized genome vaccines (PGV) and targets neoantigens predicted by our OpenVax computational pipeline. In this study, we report results from the PGV001 study (NCT02721043) targeting up to 10 neoantigens, administered in the adjuvant setting to patients with both solid and hematologic malignancies who have high risk of recurrence. Our data indicate that PGV001 is feasible and safe, with 13 of 14 enrolled patients receiving the vaccine and 11 completing the treatment. 100% of vaccinated patients developed targeted T-cell and B-cell responses, highlighting the capacity of OpenVax to predict immunogenic neoantigens and the potential of PGV001 for safely inducing targeted immunity. Significance: The PGV001 platform is feasible, safe, and immunogenic. The OpenVax pipeline predicted immunogenic neoantigens in tumors with wide-ranging mutational burdens. Data from this study prompted three additional PGV001 trials, one in newly diagnosed glioblastoma, one in urothelial cancer in combination with an ICI, and another in prostate cancer.