NetMHCcons: a consensus method for the major histocompatibility complex class I predictions

A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma

Immunologic responses to anti-PD-1 therapy in melanoma patients occur rapidly with pharmacodynamic T cell responses detectable in blood by 3 weeks. It is unclear, however, whether these early blood-based observations translate to the tumor microenvironment. We conducted a study of neoadjuvant/adjuvant anti-PD-1 therapy in stage III/IV melanoma. We hypothesized that immune reinvigoration in the tumor would be detectable at 3 weeks and that this response would correlate with disease-free survival. We identified a rapid and potent anti-tumor response, with 8 of 27 patients experiencing a complete or major pathological response after a single dose of anti-PD-1, all of whom remain disease free. These rapid pathologic and clinical responses were associated with accumulation of exhausted CD8 T cells in the tumor at 3 weeks, with reinvigoration in the blood observed as early as 1 week. Transcriptional analysis demonstrated a pretreatment immune signature (neoadjuvant response signature) that was associated with clinical benefit. In contrast, patients with disease recurrence displayed mechanisms of resistance including immune suppression, mutational escape, and/or tumor evolution. Neoadjuvant anti-PD-1 treatment is effective in high-risk resectable stage III/IV melanoma. Pathological response and immunological analyses after a single neoadjuvant dose can be used to predict clinical outcome and to dissect underlying mechanisms in checkpoint blockade.

Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma

Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations (PTPN11, BRAF) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes as well as with differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor's clonal evolution during treatment.

Most viral peptides displayed by class I MHC on infected cells are immunogenic

CD8+ T cells are essential effectors in antiviral immunity, recognizing short virus-derived peptides presented by MHC class I (pMHCI) on the surface of infected cells. However, the fraction of viral pMHCI on infected cells that are immunogenic has not been shown for any virus. To approach this fundamental question, we used peptide sequencing by high-resolution mass spectrometry to identify more than 170 vaccinia virus pMHCI presented on infected mouse cells. Next, we screened each peptide for immunogenicity in multiple virus-infected mice, revealing a wide range of immunogenicities. A surprisingly high fraction (>80%) of pMHCI were immunogenic in at least one infected mouse, and nearly 40% were immunogenic across more than half of the mice screened. The high number of peptides found to be immunogenic and the distribution of responses across mice give us insight into the specificity of antiviral CD8+ T cell responses.

CD8+ T cells from patients with narcolepsy and healthy controls recognize hypocretin neuron-specific antigens

Narcolepsy Type 1 (NT1) is a neurological sleep disorder, characterized by the loss of hypocretin/orexin signaling in the brain. Genetic, epidemiological and experimental data support the hypothesis that NT1 is a T-cell-mediated autoimmune disease targeting the hypocretin producing neurons. While autoreactive CD4+ T cells have been detected in patients, CD8+ T cells have only been examined to a minor extent. Here we detect CD8+ T cells specific toward narcolepsy-relevant peptides presented primarily by NT1-associated HLA types in the blood of 20 patients with NT1 as well as in 52 healthy controls, using peptide-MHC-I multimers labeled with DNA barcodes. In healthy controls carrying the disease-predisposing HLA-DQB1*06:02 allele, the frequency of autoreactive CD8+ T cells was lower as compared with both NT1 patients and HLA-DQB1*06:02-negative healthy individuals. These findings suggest that a certain level of CD8+ T-cell reactivity combined with HLA-DQB1*06:02 expression is important for NT1 development.

A Synthetic DNA, Multi-Neoantigen Vaccine Drives Predominately MHC Class I CD8+ T-cell Responses, Impacting Tumor Challenge

T-cell recognition of cancer neoantigens is important for effective immune-checkpoint blockade therapy, and an increasing interest exists in developing personalized tumor neoantigen vaccines. Previous studies utilizing RNA and long-peptide neoantigen vaccines in preclinical and early-phase clinical studies have shown immune responses predominantly driven by MHC class II CD4⁺ T cells. Here, we report on a preclinical study utilizing a DNA vaccine platform to target tumor neoantigens. We showed that optimized strings of tumor neoantigens, when delivered by potent electroporation-mediated DNA delivery, were immunogenic and generated predominantly MHC class I-restricted, CD8⁺ T-cell responses. High MHC class I affinity was associated specifically with immunogenic CD8⁺ T-cell epitopes. These DNA neoantigen vaccines induced a therapeutic antitumor response in vivo, and neoantigen-specific T cells expanded from immunized mice directly killed tumor cells ex vivo These data illustrate a unique advantage of this DNA platform to drive CD8⁺ T-cell immunity for neoantigen immunotherapy.

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Interactions between human leukocyte antigens (HLAs) and peptides play a critical role in the human immune system. Accurate computational prediction of HLA-binding peptides can be used for peptide drug discovery. Currently, the best prediction algorithms are neural network-based pan-specific models, which take advantage of the large amount of data across HLA alleles. However, current pan-specific models are all based on the pseudo sequence encoding for modeling the binding context, which is based on 34 positions identified from the HLA protein-peptide bound structures in early works. In this work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptide binding prediction, in which the encoding of the HLA sequence and the binding context are both learned by the network itself without requiring the HLA-peptide bound structure information. Our DCNN model is also characterized by its binding context extraction layer and dual outputs with both binding affinity output and binding probability outputs. Evaluation on public benchmark datasets shows that our DeepSeqPan model without HLA structural information in training achieves state-of-the-art performance on a large number of HLA alleles with good generalization capability. Since our model only needs raw sequences from the HLA-peptide binding pairs, it can be applied to binding predictions of HLAs without structure information and can also be applied to other protein binding problems such as protein-DNA and protein-RNA bindings. The implementation code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPan .

HIV peptidome-wide association study reveals patient-specific epitope repertoires associated with HIV control

Genetic variation in the peptide-binding groove of the highly polymorphic HLA class I molecules has repeatedly been associated with HIV-1 control and progression to AIDS, accounting for up to 12% of the variation in HIV-1 set point viral load (spVL). This suggests a key role in disease control for HLA presentation of HIV-1 epitopes to cytotoxic T cells. However, a comprehensive understanding of the relevant HLA-bound HIV epitopes is still elusive. Here we describe a peptidome-wide association study (PepWAS) approach that integrates HLA genotypes and spVL data from 6,311 HIV-infected patients to interrogate the entire HIV-1 proteome (3,252 unique peptides) for disease-relevant peptides. This PepWAS approach predicts a core set of epitopes associated with spVL, including known epitopes but also several previously uncharacterized disease-relevant peptides. More important, each patient presents only a small subset of these predicted core epitopes through their individual HLA-A and HLA-B variants. Eventually, the individual differences in these patient-specific epitope repertoires account for the variation in spVL that was previously associated with HLA genetic variation. PepWAS thus enables a comprehensive functional interpretation of the robust but little-understood association between HLA and HIV-1 control, prioritizing a short list of disease-associated epitopes for the development of targeted therapy.

Hypermutagenesis in untreated adult gliomas due to inherited mismatch mutations

Hypermutagenesis refers to marked increase in the number of mutations due to continuous mutagenic process. Hypermutated tumors, have being found in several tumor types, are associated with inherited or acquired alterations in the DNA repair pathways. Hypermutation has been observed in a subset of adult glioma patients as a direct result of temozolomide(TMZ)-induced mutagenesis. In our study, we have identified a rare subset of treatment-naïve adult gliomas with de novo hypermutator phenotype and explored the evolution of spontaneous and treatment-induced hypermutagenesis. We conducted Whole-Exome Sequencing (WES), Whole-Transcriptome Sequencing (WTS), and Single-Cell Sequencing (SCS) of TMZ-naïve and post-TMZ-treated hypermutated tumors to identify distinct clinical or genomic manifestations that contribute to the development of hypermutation in untreated adult gliomas. TMZ-naïve hypermutated tumors were marked by absence of IDH1 somatic mutation and MGMT promoter (pMGMT) methylation, two genomic traits that were significantly associated with the TMZ-induced hypermutagenic event in glioblastoma, and harbored inherited alterations in the mismatch repair (MMR) machinery. The immediate family members of the TMZ-naive hypermutated glioma patients were also previous diagnosed with cancer development history, suggesting that germline dysfunction of the MMR pathway could potentially pose hereditary risk to genetic predisposition of carcinogenesis in gliomas. Lastly, both TMZ-naïve and post-TMZ-treated hypermutated tumors exhibited a significant accumulation of neoantigen loads, suggesting immunotherapeutic alternatives. Our results present new and unique understanding of hypermutagenic process in adult gliomas and an important step towards clinical implication of immunotherapy in glioma treatment.

The role of neoantigen in immune checkpoint blockade therapy

Immune checkpoint inhibitor induces tumor rejection by activated host immune system. The anti-tumor immune response consists of capture, presentation, recognition of neoantigen, as well as subsequent killing of tumor cell. Due to the interdependence among this series of stepwise events, neoantigen profoundly influences the efficacy of anti-immune checkpoint therapy. Moreover, the neoantigen-specific T cell reactivity is the cornerstone of multiple immunotherapies. In fact, several strategies targeting neoantigen have been attempted for synergetic effect with immune checkpoint inhibitor. Increasing neoantigen presentation to immune system by oncolytic virus, radiotherapy, or cancer vaccine is feasible to enhance neoantigen-specific T cell reactivity in theory. However, some obstacles have not been overcome in practice such as dynamic variation of neoantigen landscape, identification of potential neoantigen, maintenance of high T cell titer post vaccination. In addition, adoptive T cell transfer is another approach to enhance neoantigen-specific T cell reactivity, especially for patients with severe immunosuppression. In this review, we highlighted the advancements of neoantigen and innovative explorations of utilization of neoantigen repertoire in immune checkpoint blockade therapy.

A comprehensive in silico analysis for identification of therapeutic epitopes in HPV16, 18, 31 and 45 oncoproteins

Human papillomaviruses (HPVs) are a group of circular double-stranded DNA viruses, showing severe tropism to mucosal tissues. A subset of HPVs, especially HPV16 and 18, are the primary etiological cause for several epithelial cell malignancies, causing about 5.2% of all cancers worldwide. Due to the high prevalence and mortality, HPV-associated cancers have remained as a significant health problem in human society, making an urgent need to develop an effective therapeutic vaccine against them. Achieving this goal is primarily dependent on the identification of efficient tumor-associated epitopes, inducing a robust cell-mediated immune response. Previous information has shown that E5, E6, and E7 early proteins are responsible for the induction and maintenance of HPV-associated cancers. Therefore, the prediction of major histocompatibility complex (MHC) class I T cell epitopes of HPV16, 18, 31 and 45 oncoproteins was targeted in this study. For this purpose, a two-step plan was designed to identify the most probable CD8+ T cell epitopes. In the first step, MHC-I and II binding, MHC-I processing, MHC-I population coverage and MHC-I immunogenicity prediction analyses, and in the second step, MHC-I and II protein-peptide docking, epitope conservation, and cross-reactivity with host antigens’ analyses were carried out successively by different tools. Finally, we introduced five probable CD8+ T cell epitopes for each oncoprotein of the HPV genotypes (60 epitopes in total), which obtained better scores by an integrated approach. These predicted epitopes are valuable candidates for in vitro or in vivo therapeutic vaccine studies against the HPV-associated cancers. Additionally, this two-step plan that each step includes several analyses to find appropriate epitopes provides a rational basis for DNA- or peptide-based vaccine development.

A neoepitope derived from a novel human germline APC gene mutation in familial adenomatous polyposis shows selective immunogenicity

Familial adenomatous polyposis (FAP) is an inherited condition arising from genetic defects in the Adenomatous polyposis coli (APC) gene. Carriers with mutations in the APC gene develop polyps in the colon and rectum which if not managed, transition into colon cancer. In this study, we identified a novel germline mutation in the APC gene in members of an FAP-affected (Familial adenomatous polyposis) family. This unique heterozygous variant (c.735_736insT; p.Ser246PhefsTer6) was identified in ten out of twenty six family members, ranging in age from 6 to 60 years. Polyps were detected in six of the ten individuals (35–60 years) carrying this mutation. The remaining four members (6–23 years) remain polyp free. A significant fraction of FAP affected individuals eventually develop colon cancer and therapeutic interventions to prevent cancer progression remain elusive. To address this issue, we sought to determine if peptides derived from the novel APC mutation could induce a cytotoxic T cell response, thereby qualifying them as vaccine candidates. Peptides harboring the variant amino acids were first interrogated in silico for their immunogenicity using a proprietary neoepitope prioritization pipeline, OncoPeptVAC. A single 9-mer peptide was predicted to be immunogenic. Remarkably, CD8⁺ T cells isolated from either an FAP⁺/ APC^mut individual, or from a FAP^-/ APC^mut individual, failed to respond to the peptide, whereas those from either an unaffected family member (FAP^-/ APC^wt) or from healthy unrelated donors, showed a robust response, suggesting that CD8⁺ T cells from individuals carrying this germline APC mutation have been tolerized to the mutation. Furthermore, experimental testing of six additional reported APC gene mutation-derived peptides revealed one of the six to be immunogenic. While not all APC mutant peptides are inmmunogenic, a few qualify as vaccine candidates offering novel treatment opportunities to patients with somatic APC gene mutations to delay/treat colorectal cancer.

Targeting Neoantigens for Personalised Immunotherapy

This review discusses the rapidly evolving field of immunotherapy research, focusing on the types of cancer antigens that can be recognised by the immune system and potential methods by which neoantigens can be exploited clinically to successfully target and clear tumour cells. Recent studies suggest that the likelihood of successful immunotherapeutic targeting of cancer will be reliant on immune response to neoantigens. This type of cancer-specific antigen arises from somatic variants that result in alteration of the expressed protein sequence. Massively parallel sequencing techniques now allow the rapid identification of these genomic mutations, and algorithms can be used to predict those that will be processed by the proteasome, bind to the transporter complex and encode peptides that bind strongly to individual MHC molecules. The emerging data from assessment of the immunogenicity of neoantigens suggests that only a minority of mutations will form targetable epitopes and therefore the potential for immunotherapeutic targeting will be greater in cancers with a higher frequency of protein-altering somatic variants. It is evident that neoantigens contribute to the success of some immunotherapeutic interventions and that there is significant scope for specific targeting of these antigens to develop new treatment approaches.

Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures

The urea cycle (UC) is the main pathway by which mammals dispose of waste nitrogen. We find that specific alterations in the expression of most UC enzymes occur in many tumors, leading to a general metabolic hallmark termed "UC dysregulation" (UCD). UCD elicits nitrogen diversion toward carbamoyl-phosphate synthetase2, aspartate transcarbamylase, and dihydrooratase (CAD) activation and enhances pyrimidine synthesis, resulting in detectable changes in nitrogen metabolites in both patient tumors and their bio-fluids. The accompanying excess of pyrimidine versus purine nucleotides results in a genomic signature consisting of transversion mutations at the DNA, RNA, and protein levels. This mutational bias is associated with increased numbers of hydrophobic tumor antigens and a better response to immune checkpoint inhibitors independent of mutational load. Taken together, our findings demonstrate that UCD is a common feature of tumors that profoundly affects carcinogenesis, mutagenesis, and immunotherapy response.

Identifying neoantigens for use in immunotherapy

This review focuses on the types of cancer antigens that can be recognised by the immune system and form due to alterations in the cancer genome, including cancer testis, overexpressed and neoantigens. Specifically, neoantigens can form when cancer cell-specific mutations occur that result in alterations of the protein from ‘self’. This type of antigen can result in an immune response sufficient to clear tumour cells when activated. Furthermore, studies have reported that the likelihood of successful immunotherapeutic targeting of cancer by many different methods was reliant on immune response to neoantigens. The recent resurgence of interest in the immune response to tumour cells, in conjunction with technological advances, has resulted in a large increase in the predicted, identified and functionally confirmed neoantigens. This growth in identified neoantigen sequences has increased the contents of training sets for algorithms, which in turn improves the prediction of which genetic mutations may form neoantigens. Additionally, algorithms predicting how proteins will be processed into peptide epitopes by the proteasome and which peptides bind to the transporter complex are also improving with this research. Now that large screens of all the tumour-specific protein altering mutations are possible, the emerging data from assessment of the immunogenicity of neoantigens suggest that only a minority of variants will form targetable epitopes. The potential for immunotherapeutic targeting of neoantigens will therefore be greater in cancers with a higher frequency of protein altering somatic variants. There is considerable potential in the use of neoantigens to treat patients, either alone or in combination with other immunotherapies and with continued advancements, these potentials will be realised.

A cancer vaccine approach for personalized treatment of Lynch Syndrome

Lynch syndrome (LS) is a cancer predisposition disorder wherein patients have a 70–80% lifetime risk of developing colorectal cancers (CRC). Finding germline mutations in predisposing genes allows for risk assessment of CRC development. Here we report a germline heterozygous frame-shift mutation in the mismatch repair MLH1 gene which was identified in members of two unrelated LS families. Since defects in DNA mismatch repair genes generate frame-shift mutations giving rise to highly immunogenic neoepitopes, we postulated that vaccination with these mutant peptide antigens could offer promising treatment options to LS patients. To this end we performed whole-exome and RNA seq analysis on the blood and tumour samples from an LS-CRC patient, and used our proprietary neoepitope prioritization pipeline OncoPeptVAC to select peptides, and confirm their immunogenicity in an ex vivo CD8⁺ T cell activation assay. Three neoepitopes derived from the tumour of this patient elicited a potent CD8⁺ T cell response. Furthermore, analysis of the tumour-associated immune infiltrate revealed CD8⁺ T cells expressing low levels of activation markers, suggesting mechanisms of immune suppression at play in this relapsed tumour. Taken together, our study paves the way towards development of a cancer vaccine to treat or delay the onset/relapse of LS-CRC.

Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition.

Improved methods for predicting peptide binding affinity to MHC class II molecules

Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.

Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma

We describe results from IMmotion150, a randomized phase 2 study of atezolizumab (anti-PD-L1) alone or combined with bevacizumab (anti-VEGF) versus sunitinib in 305 patients with treatment-naive metastatic renal cell carcinoma. Co-primary endpoints were progression-free survival (PFS) in intent-to-treat and PD-L1+ populations. Intent-to-treat PFS hazard ratios for atezolizumab + bevacizumab or atezolizumab monotherapy versus sunitinib were 1.0 (95% confidence interval (CI), 0.69-1.45) and 1.19 (95% CI, 0.82-1.71), respectively; PD-L1+ PFS hazard ratios were 0.64 (95% CI, 0.38-1.08) and 1.03 (95% CI, 0.63-1.67), respectively. Exploratory biomarker analyses indicated that tumor mutation and neoantigen burden were not associated with PFS. Angiogenesis, T-effector/IFN-γ response, and myeloid inflammatory gene expression signatures were strongly and differentially associated with PFS within and across the treatments. These molecular profiles suggest that prediction of outcomes with anti-VEGF and immunotherapy may be possible and offer mechanistic insights into how blocking VEGF may overcome resistance to immune checkpoint blockade.

A Targeted LC-MS Strategy for Low-Abundant HLA Class-I-Presented Peptide Detection Identifies Novel Human Papillomavirus T-Cell Epitopes

For rational design of therapeutic vaccines, detailed knowledge about target epitopes that are endogenously processed and truly presented on infected or transformed cells is essential. Many potential target epitopes (viral or mutation-derived), are presented at low abundance. Therefore, direct detection of these peptides remains a challenge. This study presents a method for the isolation and LC-MS3 -based targeted detection of low-abundant human leukocyte antigen (HLA) class-I-presented peptides from transformed cells. Human papillomavirus (HPV) was used as a model system, as the HPV oncoproteins E6 and E7 are attractive therapeutic vaccination targets and expressed in all transformed cells, but present at low abundance due to viral immune evasion mechanisms. The presented approach included preselection of target antigen-derived peptides by in silico predictions and in vitro binding assays. The peptide purification process was tailored to minimize contaminants after immunoprecipitation of HLA-peptide complexes, while keeping high isolation yields of low-abundant target peptides. The subsequent targeted LC-MS3 detection allowed for increased sensitivity, which resulted in successful detection of the known HLA-A2-restricted epitope E711-19 and ten additional E7-derived peptides on the surface of HPV16-transformed cells. T-cell reactivity was shown for all the 11 detected peptides in ELISpot assays, which shows that detection by our approach has high predictive value for immunogenicity. The presented strategy is suitable for validating even low-abundant candidate epitopes to be true immunotherapy targets.

Allele-specific Alterations in the Peptidome Underlie the Joint Association of HLA-A*29:02 and Endoplasmic Reticulum Aminopeptidase 2 (ERAP2) with Birdshot Chorioretinopathy

Virtually all patients of the rare inflammatory eye disease birdshot chorioretinopathy (BSCR) carry the HLA-A*29:02 allele. BSCR is also associated with endoplasmic reticulum aminopeptidase 2 (ERAP2), an enzyme involved in processing HLA class I ligands, thus implicating the A*29:02 peptidome in this disease. To investigate the relationship between both risk factors we employed label-free quantitative mass spectrometry to characterize the effects of ERAP2 on the A*29:02-bound peptidome. An ERAP2-negative cell line was transduced with lentiviral constructs containing GFP-ERAP2 or GFP alone, and the A*29:02 peptidomes from both transduced cells were compared. A similar analysis was performed with two additional A*29:02-positive, ERAP1-concordant, cell lines expressing or not ERAP2. In both comparisons the presence of ERAP2 affected the following features of the A*29:02 peptidome: 1) Length, with increased amounts of peptides >9-mers, and 2) N-terminal residues, with less ERAP2-susceptible and more hydrophobic ones. The paradoxical effects on peptide length suggest that unproductive binding to ERAP2 might protect some peptides from ERAP1 over-trimming. The influence on N-terminal residues can be explained by a direct effect of ERAP2 on trimming, without ruling out and improved processing in concert with ERAP1. The alterations in the A*29:02 peptidome suggest that the association of ERAP2 with BSCR is through its effects on peptide processing. These differ from those on the ankylosing spondylitis-associated HLA-B*27. Thus, ERAP2 alters the peptidome of distinct HLA molecules as a function of their specific binding preferences, influencing different pathological outcomes in an allele-dependent way.

Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer

Combination immune checkpoint blockade has demonstrated promising benefit in lung cancer, but predictors of response to combination therapy are unknown. Using whole-exome sequencing to examine non-small-cell lung cancer (NSCLC) treated with PD-1 plus CTLA-4 blockade, we found that high tumor mutation burden (TMB) predicted improved objective response, durable benefit, and progression-free survival. TMB was independent of PD-L1 expression and the strongest feature associated with efficacy in multivariable analysis. The low response rate in TMB low NSCLCs demonstrates that combination immunotherapy does not overcome the negative predictive impact of low TMB. This study demonstrates the association between TMB and benefit to combination immunotherapy in NSCLC. TMB should be incorporated in future trials examining PD-(L)1 with CTLA-4 blockade in NSCLC.

Improved Ribo-seq enables identification of cryptic translation events

Ribosome profiling has been used to predict thousands of short open reading frames (sORFs) in eukaryotic cells, but it suffers from substantial levels of noise. PRICE (https://github.com/erhard-lab/price) is a computational method that models experimental noise to enable researchers to accurately resolve overlapping sORFs and noncanonical translation initiation. We experimentally validated translation using major histocompatibility complex class I (MHC I) peptidomics and observed that sORF-derived peptides efficiently enter the MHC I presentation pathway and thus constitute a substantial fraction of the antigen repertoire.

Heterologous DNA prime-protein boost immunization with RecA and FliD offers cross-clade protection against leptospiral infection

The emergence of >300 serovars of Leptospira confounded the use of generalized bacterin, the whole cell lysate, as vaccines to control leptospirosis. Because of substantial genetic and geographic heterogeneity among circulating serovars, one vaccine strain per serovar cannot be efficacious against all the serovars. We have performed heterologous DNA prime-protein boost vaccination challenge studies in hamsters using in vivo expressed, leptospiral recombinase A (RecA) and flagellar hook associated protein (FliD). We prepared the monovalent recombinant protein, plasmid DNA, and DNA prime protein boost adjuvant vaccines. The whole cell bacterin served as a control. Our data show that (i) RecA and FliD have multiple immunogenic B and T-cell epitopes with highly conserved domains among most prevalent pathogenic Leptospira spp., (ii) humoral and cell mediated immune responses were induced remarkably, (iii) provides significant protection against homologous (Autumnalis strain N2) and cross-clade heterologous (Canicola strain PAI-1) challenge infection for the heterologous prime-protein boost (∼91–100%) and, the DNA vaccine (∼75–83%). Recombinant protein vaccine shows only partial protection (∼58–66%), (iv) RecA prime-protein boost vaccine shows sterilizing immunity, with heterologous protection. This RecA/FliD prime-protein boost strategy holds potential for vaccination against animal leptospirosis and for a better control of zoonotic transmission.

Ranking the Contribution of Ankylosing Spondylitis-associated Endoplasmic Reticulum Aminopeptidase 1 (ERAP1) Polymorphisms to Shaping the HLA-B*27 Peptidome

The Endoplasmic reticulum aminopeptidase I (ERAP1) trims peptides to their optimal size for binding to Major Histocompatibility Complex class I proteins. The natural polymorphism of this enzyme is associated with ankylosing spondylitis (AS) in epistasis with the major risk factor for this disease, HLA-B*27, suggesting a direct relationship between AS and HLA-B*27-bound peptides. Three polymorphisms that affect peptide trimming protect from AS: K528R, D575N/R725Q, and Q730E. We characterized and ranked the effects of each mutation, and their various combinations, by quantitative comparisons of the HLA-B*27 peptidomes from cells expressing distinct ERAP1 variants. Five features were examined: peptide length, N-terminal flanking residues, N-terminal residues of the natural ligands, internal sequences and affinity for B*27:05. Polymorphism at residue 528 showed the largest influence, affecting all five features regardless of peptide length. D575N/R725Q showed a much smaller effect. Yet, when co-occurring with K528R, it further decreased ERAP1 activity. Polymorphism at residue 730 showed a significant influence on peptide length, because of distinct effects on trimming of nonamers compared with longer peptides. Accordingly, multiple features were affected by the Q730E mutation in a length-dependent way. The alterations induced in the B*27:05 peptidome by natural ERAP1 variants with different K528R/Q730E combinations reflected separate and additive effects of both mutations. Thus, the influence of ERAP1 on HLA-B*27 is very diverse at the population level, because of the multiplicity and complexity of ERAP1 variants, and to the distinct effects of their co-occurring polymorphisms, leading to significant modulation of disease risk among HLA-B*27-positive individuals.

NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors

With the advancement of personalized cancer immunotherapies, new tools are needed to identify tumor antigens and evaluate T-cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the mouse mammary tumor virus-polyomavirus middle T antigen (MMTV-PyMT) mouse-an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a C57BL/6 genetic background, in order to leverage well-developed C57BL/6 immunologic tools, results in delayed tumor development and variable metastatic phenotypes. Therefore, we initiated characterization of the FVB MHC class I H-2^q haplotype to establish useful immunologic tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2^q MHC class I alleles, including >8,500 unique peptide ligands, a multiallele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer. Cancer Immunol Res; 6(6); 636-44. ©2018 AACR.

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.

PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity

Predicting peptide binding affinity with human leukocyte antigen (HLA) is a crucial step in developing powerful antitumor vaccine for cancer immunotherapy. Currently available methods work quite well in predicting peptide binding affinity with HLA alleles such as HLA-A*0201, HLA-A*0101, and HLA-B*0702 in terms of sensitivity and specificity. However, quite a few types of HLA alleles that are present in the majority of human populations including HLA-A*0202, HLA-A*0203, HLA-A*6802, HLA-B*5101, HLA-B*5301, HLA-B*5401, and HLA-B*5701 still cannot be predicted with satisfactory accuracy using currently available methods. Furthermore, currently the most popularly used methods for predicting peptide binding affinity are inefficient in identifying neoantigens from a large quantity of whole genome and transcriptome sequencing data. Here we present a Position Specific Scoring Matrix (PSSM)-based software called PSSMHCpan to accurately and efficiently predict peptide binding affinity with a broad coverage of HLA class I alleles. We evaluated the performance of PSSMHCpan by analyzing 10-fold cross-validation on a training database containing 87 HLA alleles and obtained an average area under receiver operating characteristic curve (AUC) of 0.94 and accuracy (ACC) of 0.85. In an independent dataset (Peptide Database of Cancer Immunity) evaluation, PSSMHCpan is substantially better than the popularly used NetMHC-4.0, NetMHCpan-3.0, PickPocket, Nebula, and SMM with a sensitivity of 0.90, as compared to 0.74, 0.81, 0.77, 0.24, and 0.79. In addition, PSSMHCpan is more than 197 times faster than NetMHC-4.0, NetMHCpan-3.0, PickPocket, sNebula, and SMM when predicting neoantigens from 661 263 peptides from a breast tumor sample. Finally, we built a neoantigen prediction pipeline and identified 117 017 neoantigens from 467 cancer samples of various cancers from TCGA. PSSMHCpan is superior to the currently available methods in predicting peptide binding affinity with a broad coverage of HLA class I alleles.

Exceptional Response to Pembrolizumab in a Metastatic, Chemotherapy/Radiation-Resistant Ovarian Cancer Patient Harboring a PD-L1-Genetic Rearrangement

Purpose: Ovarian carcinoma no longer responsive to surgery and chemotherapy remains an incurable disease. Alternative therapeutic options remain desperately needed.Patients and Methods: We describe a heavily pretreated patient with ovarian cancer with recurrent disease experiencing a remarkable clinical response to treatment with the anti-PD1 immune checkpoint inhibitor pembrolizumab. The clinical, pathological, and genomic characteristics of this exceptional ovarian cancer responder were carefully investigated using immunohistochemistry (IHC), quantitative multiplex fluorescence methods (i.e., automated quantitative analysis, AQUA) and whole-exome sequencing (WES) techniques.Results: The patient harbored a recurrent/metastatic radiation and chemotherapy-resistant high-grade ovarian carcinoma with clear cell features. While progressing on any standard treatment modality, she demonstrated a remarkable complete response to the anti-PD1 immune checkpoint inhibitor pembrolizumab. WES results were notable for the presence a relative low number of mutations (tumor mutation load/Mb = 4.31, total mutations = 164) and a peculiar structural variant disrupting the 3' region of the PD-L1 gene causing aberrant PD-L1 surface expression as confirmed by IHC and AQUA technology. Heavy infiltration of the PD-L1-mutated and PD-L1-overexpressing tumor with T-cell lymphocytes (i.e., CD4⁺/CD8⁺ TIL), CD68⁺ macrophages, and CD20⁺ B cells was detected in the surgical specimen strongly suggesting immune evasion as a key mechanism of tumor growth and survival. Patient's complete clinical responses remain unchanged at the time of the writing of this report with no significant side effects reported to date.Conclusions: Anti-PD1 inhibitors may represent a novel treatment option for recurrent/metastatic human tumors refractory to salvage treatment harboring PD-L1 gene structural variations causing aberrant PD-L1 expression. Clin Cancer Res; 24(14); 3282-91. ©2018 AACRSee related commentary by Lheureux, p. 3233.

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 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.

Bioinformatics Tools for the Prediction of T-Cell Epitopes

T-cell responses are activated by specific peptides, called epitopes, presented on the cell surface by MHC molecules. Binding of peptides to the MHC is the most selective step in T-cell antigen presentation and therefore an essential factor in the selection of potential epitopes. Several in-vitro methods have been developed for the determination of peptide binding to MHC molecules, but these are all costly and time-consuming. In consequence, significant effort has been dedicated to the development of in-silico methods to model this event. Here, we describe two such tools, NetMHCcons and NetMHCIIpan, for the prediction of peptide binding to MHC class I and class II molecules, respectively, involved in the activation pathways of CD8+ and CD4+ T cells.

Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics

The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.

Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data

Peptide binding to MHC class I molecules is the single most selective step in antigen presentation and the strongest single correlate to peptide cellular immunogenicity. The cost of experimentally characterizing the rules of peptide presentation for a given MHC-I molecule is extensive, and predictors of peptide–MHC interactions constitute an attractive alternative. Recently, an increasing amount of MHC presented peptides identified by mass spectrometry (MS ligands) has been published. Handling and interpretation of MS ligand data is, in general, challenging due to the polyspecificity nature of the data. We here outline a general pipeline for dealing with this challenge and accurately annotate ligands to the relevant MHC-I molecule they were eluted from by use of GibbsClustering and binding motif information inferred from in silico models. We illustrate the approach here in the context of MHC-I molecules (BoLA) of cattle. Next, we demonstrate how such annotated BoLA MS ligand data can readily be integrated with in vitro binding affinity data in a prediction model with very high and unprecedented performance for identification of BoLA-I restricted T-cell epitopes. The prediction model is freely available at http://www.cbs.dtu.dk/services/NetMHCpan/NetBoLApan. The approach has here been applied to the BoLA-I system, but the pipeline is readily applicable to MHC systems in other species.

Building proteomic tool boxes to monitor MHC class I and class II peptides

Major histocompatibility complex Class I (MHCI) and Class II (MHCII) presented peptides powerfully modulate T cell immunity and play a vital role in generating effective anti-tumor and anti-viral immune responses in mammals. Characterizing these MHCI or MHCII presented peptides can help generate therapeutic treatments, afford information on T cell mediated biomarkers, provide insight into disease progression, and reduce adverse anti-drug side effects from engineered biotherapeutics. Here, we explore the tools and techniques commonly employed to discover both MHCI- and MHCII-presented peptides. We describe complementary strategies that enhance the characterization of these peptides and the informatics tools employed for both predicting and characterizing MHCI- and MHCII-presented epitopes. The evolution of methodologies for isolating MHC-presented peptides is discussed, as are the mass spectrometric workflows that can be employed for their characterization. We provide a perspective on where this field is headed, and how these tools may be applicable to the discovery and monitoring of epitopes in a variety of scenarios.

The neoepitope landscape in pediatric cancers

BACKGROUND

Neoepitopes derived from tumor-specific somatic mutations are promising targets for immunotherapy in childhood cancers. However, the potential for such therapies in targeting these epitopes remains uncertain due to a lack of knowledge of the neoepitope landscape in childhood cancer. Studies to date have focused primarily on missense mutations without exploring gene fusions, which are a major class of oncogenic drivers in pediatric cancer.

METHODS

We developed an analytical workflow for identification of putative neoepitopes based on somatic missense mutations and gene fusions using whole-genome sequencing data. Transcriptome sequencing data were incorporated to interrogate the expression status of the neoepitopes.

RESULTS

We present the neoepitope landscape of somatic alterations including missense mutations and oncogenic gene fusions identified in 540 childhood cancer genomes and transcriptomes representing 23 cancer subtypes. We found that 88% of leukemias, 78% of central nervous system tumors, and 90% of solid tumors had at least one predicted neoepitope. Mutation hotspots in KRAS and histone H3 genes encode potential epitopes in multiple patients. Additionally, the ETV6-RUNX1 fusion was found to encode putative neoepitopes in a high proportion (69.6%) of the pediatric leukemia harboring this fusion.

CONCLUSIONS

Our study presents a comprehensive repertoire of potential neoepitopes in childhood cancers, and will facilitate the development of immunotherapeutic approaches designed to exploit them. The source code of the workflow is available at GitHub ( https://github.com/zhanglabstjude/neoepitope ).

The major histocompatibility complex class I immunopeptidome of extracellular vesicles

Extracellular vesicles (EVs) are released by most cell types and have been associated with multiple immunomodulatory functions. MHC class I molecules have crucial roles in antigen presentation and in eliciting immune responses and are known to be incorporated into EVs. However, the MHC class I immunopeptidome of EVs has not been established. Here, using a small-scale immunoisolation of the antigen serotypes HLA-A*02:01 and HLA-B*27:05 expressed on the Epstein-Barr virus-transformed B cell line Jesthom and MS of the eluted peptides from both cells and EVs, we identified 516 peptides that bind either HLA-A*02:01 or HLA-B*27:05. Of importance, the predicted serotype-binding affinities and peptide-anchor motifs did not significantly differ between the peptide pools isolated from cells or EVs, indicating that during EV biogenesis, no obvious editing of the MHC class I immunopeptidome occurs. These results, for the first time, establish EVs as a source of MHC class I peptides that can be used for the study of the immunopeptidome and in the discovery of potential neoantigens for immunotherapies.

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity

The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.

Predicting the differences between cancer and normal cells that are visible to the immune system is of central importance for cancer immunotherapy. Here we introduce a novel computational framework to harness the wealth of data from in-depth HLA peptidomics studies, including ten novel high quality (<1% FDR) datasets generated for this work, to improve predictions of peptides displayed on HLA-I molecules. These high-throughput and unbiased data enable us to refine models of HLA-I binding specificity for many alleles (including some that had no ligand until this study) and improve predictions of neo-antigens from exome sequencing data in melanoma and lung cancer samples. Moreover, the refined description of HLA-I binding specificity reveals cases of allosteric modulation of HLA-I binding specificity at the second amino acid position (P2) of their ligands by residues that are part of the HLA-I binding site but outside of the B pocket.

Cancer immunotherapy: moving forward with peptide T cell vaccines

Recent advances in cancer immunology, such as the discovery of immune checkpoint inhibitors, have validated immune cells as potential key players for effective cancer treatment. The efficacy of these therapies seems to be codependent on a tumor-reactive T lymphocyte response. For many years, numerous attempts and strategies in developing vaccines to generate tumor-reactive T cells have yielded poor results in the clinic due to suboptimal immunogenicity and the inability to overcome an immunosuppressive tumor microenvironment. In this review, we summarize past and current advances in T cell vaccines and describe our experience in developing optimized methods for antigen/adjuvant selection and vaccine administration in order to induce powerful anti-tumor responses.

Functional Interaction of the Ankylosing Spondylitis-Associated Endoplasmic Reticulum Aminopeptidase 2 With the HLA-B*27 Peptidome in Human Cells

OBJECTIVE

To determine the influence of endoplasmic reticulum aminopeptidase 2 (ERAP-2) expression on the HLA-B*27 peptidome in live cells.

METHODS

Using immunoaffinity chromatography and acid extraction, HLA-B*27:05-bound peptides were isolated from 2 ERAP-2-negative lymphoblastoid cell lines and 1 ERAP-2-positive lymphoblastoid cell line expressing functionally indistinguishable ERAP-1 variants. More than 2,000-4,000 B*27:05 ligands were identified from each cell line, and their relative abundance was established by quantitative tandem mass spectrometry and MaxQuant-based peptide analyses. Pairwise comparisons were used to determine the structural features of peptides whose relative abundance was dependent on the presence of ERAP-2. Synthetic peptide digestions were performed with recombinant ERAP-1 and ERAP-2. Peptide affinity was estimated with standard algorithms.

RESULTS

The B*27:05 peptidome from ERAP-2-positive cells showed 3-4% fewer peptides with N-terminal basic residues than did the peptidome from ERAP-2-negative cells. Among the shared peptides, those most abundant in the presence of ERAP-2 included more nonamers, fewer decamers, and fewer N-terminal basic residues than the peptides predominant in ERAP-2-negative cells. These ERAP-2-dependent changes did not alter the global affinity of the B*27:05 peptidome.

CONCLUSION

ERAP-2 significantly influences the B*27:05-bound peptidome by destroying some ligands and decreasing the abundance of many more ligands with N-terminal basic residues, while increasing the abundance of nonamers. The former effects are best explained by direct ERAP-2 trimming. The effects on peptide length might be attributed to ERAP-2-induced activation of ERAP-1 trimming. These data support the notion of a peptide-mediated mechanism as the basis for the association of ERAP-2 with ankylosing spondylitis. Analogous effects on other major histocompatibility complex class I peptidomes might explain the involvement of ERAP-2 in HLA-B27-negative spondyloarthritis.

Novel CTL epitopes identified through a Y. pestis proteome-wide analysis in the search for vaccine candidates against plague

The causative agent of Plague, Yersinia pestis, is a highly virulent pathogen and a potential bioweapon. Depending on the route of infection, two prevalent occurrences of the disease are known, bubonic and pneumonic. The latter has a high fatality rate. In the absence of a licensed vaccine, intense efforts to develop a safe and efficacious vaccine have been conducted, and humoral-driven subunit vaccines containing the F1 and LcrV antigens are currently under clinical trials. It is well known that a cellular immune response might have an essential additive value to immunity and protection against Y. pestis infection. Nevertheless, very few documented epitopes eliciting a protective T-cell response have been reported. Here, we present a combined high throughput computational and experimental effort towards identification of CD8 T-cell epitopes. All 4067 proteins of Y. pestis were analyzed with state-of-the-art recently developed prediction algorithms aimed at mapping potential MHC class I binders. A compilation of the results obtained from several prediction methods revealed a total of 238,000 peptide candidates, which necessitated downstream filtering criteria. Our previously established and proven approach for enrichment of true positive CTL epitopes, which relies on mapping clusters rich in tandem or overlapping predicted MHC binders ("hotspots"), was applied, as well as considerations of predicted binding affinity. A total of 1532 peptides were tested for their ability to elicit a specific T-cell response by following the production of IFNγ from splenocytes isolated from vaccinated mice. Altogether, the screen resulted in 178 positive responders (11.8%), all novel Y. pestis CTL epitopes. These epitopes span 113 Y. pestis proteins. Substantial enrichment of membrane-associated proteins was detected for epitopes selected from hotspots of predicted MHC binders. These results considerably expand the repertoire of known CTL epitopes in Y. pestis and pave the way to attest their protective potential, and hence their contribution to a future potent subunit vaccine.

Characterizing neoantigens for personalized cancer immunotherapy

Somatic mutations can generate neoantigens that are presented on MHC molecules and drive effective T cells responses against cancer. Mutation load in cancer patients predicts response to immune checkpoint blockade therapy. Additionally, vaccination targeting neoantigens controls established tumor growth in preclinical models. These recent findings led to a renewed interest in the field of cancer vaccines and the development of antigen-targeted cancer immunotherapies. However, targeting neoantigens is challenging, as most mutations are unique to each cancer patient. In addition, only a small fraction of the mutations are immunogenic and therefore their accurate prediction is critical. In this review, we discuss the properties of neoantigens that influence their immunogenicity, along with questions that remain to be addressed in order to improve prediction algorithms.

The Behçet's disease-associated variant of the aminopeptidase ERAP1 shapes a low-affinity HLA-B*51 peptidome by differential subpeptidome processing

A low-activity variant of endoplasmic reticulum aminopeptidase 1 (ERAP1), Hap10, is associated with the autoinflammatory disorder Behçet's disease (BD) in epistasis with HLA-B*51, which is the main risk factor for this disorder. The role of Hap10 in BD pathogenesis is unknown. We sought to define the effects of Hap10 on the HLA-B*51 peptidome and to distinguish these effects from those due to HLA-B*51 polymorphisms unrelated to disease. The peptidome of the BD-associated HLA-B*51:08 subtype expressed in a Hap10-positive cell line was isolated, characterized by mass spectrometry, and compared with the HLA-B*51:01 peptidome from cells expressing more active ERAP1 allotypes. We additionally performed synthetic peptide digestions with recombinant ERAP1 variants and estimated peptide-binding affinity with standard algorithms. In the BD-associated ERAP1 context of B*51:08, longer peptides were generated; of the two major HLA-B*51 subpeptidomes with Pro-2 and Ala-2, the former one was significantly reduced, and the latter was increased and showed more ERAP1-susceptible N-terminal residues. These effects were readily explained by the low activity of Hap10 and the differential susceptibility of X-Pro and X-Ala bonds to ERAP1 trimming and together resulted in a significantly altered peptidome with lower affinity. The differences due to ERAP1 were clearly distinguished from those due to HLA-B*51 subtype polymorphism, which affected residue frequencies at internal positions of the peptide ligands. The alterations in the nature and affinity of HLA-B*51·peptide complexes probably affect T-cell and natural killer cell recognition, providing a sound basis for the joint association of ERAP1 and HLA-B*51 with BD.

MHC-I Ligand Discovery Using Targeted Database Searches of Mass Spectrometry Data: Implications for T-Cell Immunotherapies

Class I major histocompatibility complex (MHC-I)-bound peptide ligands dictate the activation and specificity of CD8⁺ T cells and thus are important for devising T-cell immunotherapies. In recent times, advances in mass spectrometry (MS) have enabled the precise identification of these MHC-I peptides, wherein MS spectra are compared against a reference proteome. Unfortunately, matching these spectra to reference proteome databases is hindered by inflated search spaces attributed to a lack of enzyme restriction in the searches, limiting the efficiency with which MHC ligands are discovered. Here we offer a solution to this problem whereby we developed a targeted database search approach and accompanying tool SpectMHC, that is based on a priori-predicted MHC-I peptides. We first validated the approach using MS data from two different allotype-specific immunoprecipitates for the C57BL/6 mouse background. We then developed allotype-specific HLA databases to search previously published MS data sets of human peripheral blood mononuclear cells (PBMCs). This targeted search strategy improved peptide identifications for both mouse and human ligandomes by greater than 2-fold and is superior to traditional "no enzyme" searches of reference proteomes. Our targeted database search promises to uncover otherwise missed novel T-cell epitopes of therapeutic potential.

The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design

The task of epitope discovery and vaccine design is increasingly reliant on bioinformatics analytic tools and access to depositories of curated data relevant to immune reactions and specific pathogens. The Immune Epitope Database and Analysis Resource (IEDB) was indeed created to assist biomedical researchers in the development of new vaccines, diagnostics, and therapeutics. The Analysis Resource is freely available to all researchers and provides access to a variety of epitope analysis and prediction tools. The tools include validated and benchmarked methods to predict MHC class I and class II binding. The predictions from these tools can be combined with tools predicting antigen processing, TCR recognition, and B cell epitope prediction. In addition, the resource contains a variety of secondary analysis tools that allow the researcher to calculate epitope conservation, population coverage, and other relevant analytic variables. The researcher involved in vaccine design and epitope discovery will also be interested in accessing experimental published data, relevant to the specific indication of interest. The database component of the IEDB contains a vast amount of experimentally derived epitope data that can be queried through a flexible user interface. The IEDB is linked to other pathogen-specific and immunological database resources.

Separate effects of the ankylosing spondylitis associated ERAP1 and ERAP2 aminopeptidases determine the influence of their combined phenotype on the HLA-B*27 peptidome

Ankylosing spondylitis (AS) is an inflammatory disease strongly associated with the Major Histocompatibility Complex class I (MHC-I) allotype HLA-B*27. The endoplasmic reticulum aminopeptidases (ERAP)1 and 2, which trim peptides to their optimal length for MHC-I binding, are also susceptibility factors for this disease. Both highly active ERAP1 variants and ERAP2 expression favor AS, whereas loss-of-function ERAP1 and loss-of-expression ERAP2 variants are protective. Yet, only ERAP1 is in epistasis with HLA-B*27. We addressed two issues concerning the functional interaction of ERAP1 and ERAP2 with the HLA-B*27 peptidome in human cells: 1) distinguishing the effects of ERAP1 from those of ERAP2, and 2) determining the influence of ERAP2 in distinct ERAP1 contexts. Quantitative comparisons of the HLA-B*27:05 peptidomes from cells with various ERAP1/ERAP2 phenotypes were carried out. When cells expressing ERAP2 and either high or low activity ERAP1 variants were compared, increased amounts of nonamers, relative to longer ligands, and decreased amounts of peptides with Ala1, were observed in the more active ERAP1 context. When cells expressing ERAP2 in a low activity ERAP1 context or lacking ERAP2 but expressing a highly active ERAP1 variant were compared, the same effects on peptide length and Ala1, but also significantly lower amounts of peptides with N-terminal basic residues and lower affinity of the peptidome, were observed in the ERAP2-positive context. Thus, ERAP1 and ERAP2 have significant and distinct effects on the HLA-B*27 peptidome, suggesting that both enzymes largely act as separate entities in vivo. This may explain their different patterns of association with AS.

T-Cell Epitope Prediction

An epitope is a part of an immunogenic protein that can be recognized by the immune system. The peptides that can be recognized by the T-cell receptors after a particular antigen has been intracellularly processed, bound to at least one MHC molecule and expressed on the surface of the antigen presenting cell as a MHC-peptide complex, are called a T-cell epitope. Individuals who have at least one MHC molecule able to most avidly bind to allergenic amino acid sequences from an allergen, and at the same time have the appropriate T-cell clone that can recognize this MHC-peptide complex, are expected to be genetically prone to allergic reactions against that allergen. This possibility can be examined in silico by utilizing modern computational techniques that are based on sophisticated mathematics and statistics. The design principles of these techniques are different and therefore variations in their predictions are expected. The available software programs that have been developed on this basis are able to combine the increasing amount and complexity of the available experimental data that have been organized in immunoinformatics databases to predict potential allergen T-cell epitopes. All relevant T-cell epitope prediction methods can be accessed online as a freeware.