Next-generation IEDB tools: a platform for epitope prediction and analysis.

The Next-Generation (NG) IEDB Tools website (https://nextgen-tools.iedb.org) provides users with a redesigned interface to many of the algorithms for epitope prediction and analysis that were originally released on the legacy IEDB Tools website. The initial release focuses on consolidation of all tools related to HLA class I epitopes (MHC binding, elution, immunogenicity, and processing), making all of these predictions accessible from a single application and allowing for their simultaneous execution with minimal user inputs. Additionally, the PEPMatch tool for identifying highly similar epitopes in a set of curated proteomes, as well as a tool for epitope clustering, are available on the site. The NG Tools site allows users to build data pipelines by sending the output of one tool as input for the next. Over the next several years, all pre-existing IEDB Tools, and any newly developed tools, will be integrated into this new site. Here we describe the philosophy behind the redesign and demonstrate the utility and productivity enhancements that are enabled by the new interface.

Autoimmunity Against Surfactant Protein B Is Associated with Pneumonitis During Checkpoint Blockade.

Rationale: Immune checkpoint inhibitor (ICI)-related pneumonitis is a serious autoimmune event affecting as many as 20% of patients with non-small-cell lung cancer (NSCLC), yet the factors underpinning its development in some patients and not others are poorly understood. Objectives: To investigate the role of autoantibodies and autoreactive T cells against surfactant-related proteins in the development of pneumonitis. Methods: The study cohort consisted of patients with NSCLC who provided blood samples before and during ICI treatment. Serum was used for proteomics analyses and to detect autoantibodies present during pneumonitis. T-cell stimulation assays and single-cell RNA sequencing were performed to investigate the specificity and functionality of peripheral autoreactive T cells. The findings were confirmed in a validation cohort comprising patients with NSCLC and patients with melanoma. Measurements and Main Results: Across both cohorts, patients in whom pneumonitis developed had higher pretreatment levels of immunoglobulin G autoantibodies targeting surfactant protein (SP)-B. At the onset of pneumonitis, these patients also exhibited higher frequencies of CD4+ IFN-γ-positive SP-B-specific T cells and expanding T-cell clonotypes recognizing this protein, accompanied by a proinflammatory serum proteomic profile. Conclusions: Our data suggest that the cooccurrence of SP-B-specific immunoglobulin G autoantibodies and CD4+ T cells is associated with the development of pneumonitis during ICI therapy. Pretreatment levels of these antibodies may represent a potential biomarker for an increased risk of developing pneumonitis, and on-treatment levels may provide a diagnostic aid.

The Cancer Epitope Database and Analysis Resource (CEDAR).

We established The Cancer Epitope Database and Analysis Resource (CEDAR) to catalog all epitope data in the context of cancer. The specific molecular targets of adaptive T cell and B cell immune responses are referred to as epitopes. Epitopes derived from cancer antigens are of high relevance as they are recognized by anti-cancer immune cells. Detailed knowledge of the molecular characteristic of cancer epitopes and associated metadata is relevant to understanding and planning prophylactic and therapeutic applications and accurately characterizing naturally occurring immune responses and cancer immunopathology. CEDAR provides a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and serves as a companion site to the Immune Epitope Database (IEDB), which is focused on infectious, autoimmune, and allergic diseases. CEDAR is freely accessible at https://cedar.iedb.org/.

PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins.

BACKGROUND: Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. RESULTS: We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch's code and benchmark datasets are publicly available. CONCLUSIONS: PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .

Allele-Specific Thresholds of Eluted Ligands for T-Cell Epitope Prediction.

A common strategy for predicting candidate human leukocyte antigen class I T-cell epitopes is to use an affinity-based threshold of 500 nM. Although a 500 nM threshold across alleles effectively identifies most epitopes across alleles, findings showing that major histocompatibility complex repertoire sizes vary by allele indicate that using thresholds specific to individual alleles may improve epitope identification. In this work, we compare different strategies utilizing common and allele-specific thresholds to identify an optimal approach for T-cell epitope prediction. First, we confirmed previous observations that different human leukocyte antigen class I alleles correspond with varying repertoire sizes. Here, we define general and allele-specific thresholds that capture 80% of eluted ligands and a different set of thresholds associated with capturing 9-mer T-cell epitopes at 80% sensitivity. Our analysis revealed that allele-specific threshold performance was roughly equivalent to that of a common threshold when considering a relatively large number of alleles. However, when predicting epitopes for only a few alleles, the use of allele-specific thresholds would be preferable. Finally, we present here for public use a set of allele-specific thresholds that may be used to identify T-cell epitopes at 80% sensitivity.

Impact of Cysteine Residues on MHC Binding Predictions and Recognition by Tumor-Reactive T Cells.

The availability of MHC-binding prediction tools has been useful in guiding studies aimed at identifying candidate target Ags to generate reactive T cells and to characterize viral and tumor-reactive T cells. Nevertheless, prediction algorithms appear to function poorly for epitopes containing cysteine (Cys) residues, which can oxidize and form disulfide bonds with other Cys residues under oxidizing conditions, thus potentially interfering with their ability to bind to MHC molecules. Analysis of the results of HLA-A*02:01 class I binding assays carried out in the presence and absence of the reducing agent 2-ME indicated that the predicted affinity for 25% of Cys-containing epitopes was underestimated by a factor of 3 or more. Additional analyses were undertaken to evaluate the responses of human CD8+ tumor-reactive T cells against 10 Cys-containing HLA class I-restricted minimal determinants containing substitutions of α-aminobutyric acid (AABA), a cysteine analogue containing a methyl group in place of the sulfhydryl group present in Cys, for the native Cys residues. Substitutions of AABA for Cys at putative MHC anchor positions often significantly enhanced T cell recognition, whereas substitutions at non-MHC anchor positions were neutral, except for one epitope where this modification abolished T cell recognition. These findings demonstrate the need to evaluate MHC binding and T cell recognition of Cys-containing peptides under conditions that prevent Cys oxidation, and to adjust current prediction binding algorithms for HLA-A*02:01 and potentially additional class I alleles to more accurately rank peptides containing Cys anchor residues.

Comparison of HLA ligand elution data and binding predictions reveals varying prediction performance for the multiple motifs recognized by HLA-DQ2.5.

Binding prediction tools are commonly used to identify peptides presented on MHC class II molecules. Recently, a wealth of data in the form of naturally eluted ligands has become available and discrepancies between ligand elution data and binding predictions have been reported. Quantitative metrics for such comparisons are currently lacking. In this study, we assessed how efficiently MHC class II binding predictions can identify naturally eluted peptides, and investigated instances with discrepancies between the two methods in detail. We found that, in general, MHC class II eluted ligands are predicted to bind to their reported restriction element with high affinity. But, for several studies reporting an increased number of ligands that were not predicted to bind, we found that the reported MHC restriction was ambiguous. Additional analyses determined that most of the ligands predicted to not bind, are predicted to bind other co-expressed MHC class II molecules. For selected alleles, we addressed discrepancies between elution data and binding predictions by experimental measurements and found that predicted and measured affinities correlate well. For DQA1*05:01/DQB1*02:01 (DQ2.5) however, binding predictions did miss several peptides that were determined experimentally to be binders. For these peptides and several known DQ2.5 binders, we determined key residues for conferring DQ2.5 binding capacity, which revealed that DQ2.5 utilizes two different binding motifs, of which only one is predicted effectively. These findings have important implications for the interpretation of ligand elution data and for the improvement of MHC class II binding predictions.

In vitro induction of neoantigen-specific T cells in myelodysplastic syndrome, a disease with low mutational burden.

Therapies that utilize immune checkpoint inhibition work by leveraging mutation-derived neoantigens and have shown greater clinical efficacy in tumors with higher mutational burden. Whether tumors with a low mutational burden are susceptible to neoantigen-targeted therapy has not been fully addressed. To examine the feasibility of neoantigen-specific adoptive T-cell therapy, the authors studied the T-cell response against somatic variants in five patients with myelodysplastic syndrome (MDS), a malignancy with a very low tumor mutational burden. DNA and RNA from tumor (CD34+) and normal (CD3+) cells isolated from the patients' blood were sequenced to predict patient-specific MDS neopeptides. Neopeptides representing the somatic variants were used to induce and expand autologous T cells ex vivo, and these were systematically tested in killing assays to determine the proportion of neopeptides yielding neoantigen-specific T cells. The authors identified a total of 32 somatic variants (four to eight per patient) and found that 21 (66%) induced a peptide-specific T-cell response and 19 (59%) induced a T-cell response capable of killing autologous tumor cells. Of the 32 somatic variants, 11 (34%) induced a CD4+ response and 11 (34%) induced a CD8+ response that killed the tumor. These results indicate that in vitro induction of neoantigen-specific T cells is feasible for tumors with very low mutational burden and that this approach warrants investigation as a therapeutic option for such patients.

A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes.

Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.

A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer.

The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.