Is the Immunopeptidome Getting Darker?: A Commentary on the Discussion around Mishto et al., 2019

Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy

Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.

Cryptic MHC-E epitope from influenza elicits a potent cytolytic T cell response

The extent to which unconventional forms of antigen presentation drive T cell immunity is unknown. By convention, CD8 T cells recognize viral peptides, or epitopes, in association with classical major histocompatibility complex (MHC) class I, or MHC-Ia, but immune surveillance can, in some cases, be directed against peptides presented by nonclassical MHC-Ib, in particular the MHC-E proteins (Qa-1 in mice and HLA-E in humans); however, the overall importance of nonclassical responses in antiviral immunity remains unclear. Similarly uncertain is the importance of 'cryptic' viral epitopes, defined as those undetectable by conventional mapping techniques. Here we used an immunopeptidomic approach to search for unconventional epitopes that drive T cell responses in mice infected with influenza virus A/Puerto Rico/8/1934. We identified a nine amino acid epitope, termed M-SL9, that drives a co-immunodominant, cytolytic CD8 T cell response that is unconventional in two major ways: first, it is presented by Qa-1, and second, it has a cryptic origin, mapping to an unannotated alternative reading frame product of the influenza matrix gene segment. Presentation and immunogenicity of M-SL9 are dependent on the second AUG codon of the positive sense matrix RNA segment, suggesting translation initiation by leaky ribosomal scanning. During influenza virus A/Puerto Rico/8/1934 infection, M-SL9-specific T cells exhibit a low level of egress from the lungs and strong differentiation into tissue-resident memory cells. Importantly, we show that M-SL9/Qa-1-specific T cells can be strongly induced by messenger RNA vaccination and that they can mediate antigen-specific cytolysis in vivo. Our results demonstrate that noncanonical translation products can account for an important fraction of the T cell repertoire and add to a growing body of evidence that MHC-E-restricted T cells could have substantial therapeutic value.

The biogenesis of the immunopeptidome

The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.

Large-Scale Immuno-Peptidome Analysis Reveals Recurrent Post-Translational Splicing of Cancer and Immune-Associated Genes

Post-translational spliced peptides (PTSPs) are a unique class of peptides that have been found to be presented by HLA-class-I molecules in cancer (1). Thus far, no consensus has been reached on the proportion of PTSPs in the immunopeptidome, with estimates ranging from 2% to as high as 45% and stirring significant debate (2-8). Furthermore, the role of the HLA-class-II pathway in PTSP presentation has been studied only in diabetes (9). Here, we exploit our large-scale cancer peptidomics database and our newly devised pipeline for filtering spliced peptide predictions to identify recurring spliced peptides, both for HLA-class-I and -II complexes. Our results indicate that HLA-class-I spliced peptides account for a low percentage of the immunopeptidome (less than 3.1%), yet are larger in number relative to other types of identified aberrant peptides. Therefore, spliced peptides significantly contribute to the repertoire of presented peptides in cancer cells. In addition, we identified HLA-class-II-bound spliced peptides, but to a lower extent (less than 0.5%). The identified spliced peptides include cancer- and immune-associated genes, such as the MITF oncogene, DAPK1 tumor suppressor and HLA-E, which were validated using synthetic peptides. The potential immunogenicity of the DAPK1- and HLA-E-derived PTSPs was also confirmed. In addition, a reanalysis of our published mouse single-cell clone immunopeptidome dataset showed that most of the spliced peptides were found repeatedly in a large number of the single-cell clones. Establishing a novel search-scheme for the discovery and evaluation of recurring PTSPs among cancer patients may assist in identifying potential novel targets for immunotherapy.

Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes

Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines - that is, Mascot, Mascot+Percolator, and PEAKS DB - as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by MS to benchmark methods' performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.

The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome

INTRODUCTION

The comprehensive collection of peptides presented by Major Histocompatibility Complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell based therapeutics and vaccines. These applications are however challenged by the complex nature of immunopeptidome data.

AREAS COVERED

Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches.

EXPERT OPINION

Further we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large scale immunopeptidomics data.

Mechanistic diversity in MHC class I antigen recognition

Throughout its evolution, the human immune system has developed a plethora of strategies to diversify the antigenic peptide sequences that can be targeted by the CD8+ T cell response against pathogens and aberrations of self. Here we provide a general overview of the mechanisms that lead to the diversity of antigens presented by MHC class I complexes and their recognition by CD8+ T cells, together with a more detailed analysis of recent progress in two important areas that are highly controversial: the prevalence and immunological relevance of unconventional antigen peptides; and cross-recognition of antigenic peptides by the T cell receptors of CD8+ T cells.

Commentary: Are There Indeed Spliced Peptides in the Immunopeptidome?

Proteasome-generated spliced epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry triggered heated debates, which find a representative opinion in one of the two fronts in the recent perspective article by Arie Admon. Briefly, he suggests that proteasomes cannot efficiently catalyze such a reaction, and, thus, that all spliced peptides identified in HLA class I immunopeptidomes and other specimens are artifacts. This hypothesis is in contrast with in vitro, in cellula, and in vivo results published since the discovery of proteasome-catalyzed peptide splicing in 2004.