HSPVdb--the Human Short Peptide Variation Database for improved mass spectrometry-based detection of polymorphic HLA-ligands

A Roadmap Toward the Definition of Actionable Tumor-Specific Antigens

The search for tumor-specific antigens (TSAs) has considerably accelerated during the past decade due to the improvement of proteogenomic detection methods. This provides new opportunities for the development of novel antitumoral immunotherapies to mount an efficient T cell response against one or multiple types of tumors. While the identification of mutated antigens originating from coding exons has provided relatively few TSA candidates, the possibility of enlarging the repertoire of targetable TSAs by looking at antigens arising from non-canonical open reading frames opens up interesting avenues for cancer immunotherapy. In this review, we outline the potential sources of TSAs and the mechanisms responsible for their expression strictly in cancer cells. In line with the heterogeneity of cancer, we propose that discrete families of TSAs may be enriched in specific cancer types.

Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine

The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies.

Identification of Biological Relevant Minor Histocompatibility Antigens within the B-lymphocyte-Derived HLA-Ligandome Using a Reverse Immunology Approach

PURPOSE

T-cell recognition of minor histocompatibility antigens (MiHA) not only plays an important role in the beneficial graft-versus-leukemia (GVL) effect of allogeneic stem cell transplantation (allo-SCT) but also mediates serious GVH complications associated with allo-SCT. Using a reverse immunology approach, we aim to develop a method enabling the identification of T-cell responses directed against predefined antigens, with the goal to select those MiHAs that can be used clinically in combination with allo-SCT.

EXPERIMENTAL DESIGN

In this study, we used a recently developed MiHA selection algorithm to select candidate MiHAs within the HLA-presented ligandome of transformed B cells. From the HLA-presented ligandome that predominantly consisted of monomorphic peptides, 25 polymorphic peptides with a clinically relevant allele frequency were selected. By high-throughput screening, the availability of high-avidity T cells specific for these MiHA candidates in different healthy donors was analyzed.

RESULTS

With the use of MHC multimer enrichment, analyses of expanded T cells by combinatorial coding MHC multimer flow cytometry, and subsequent single-cell cloning, positive T-cell clones directed to two new MiHA: LB-CLYBL-1Y and LB-TEP1-1S could be demonstrated, indicating the immunogenicity of these two MiHAs.

CONCLUSIONS

The biologic relevance of MiHA LB-CLYBL-1Y was demonstrated by the detection of LB-CLYBL-1Y-specific T cells in a patient suffering from acute myeloid leukemia (AML) that experienced an anti-leukemic response after treatment with allo-SCT.

In silico Derivation of HLA-Specific Alloreactivity Potential from Whole Exome Sequencing of Stem-Cell Transplant Donors and Recipients: Understanding the Quantitative Immunobiology of Allogeneic Transplantation

Donor T-cell mediated graft versus host (GVH) effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the human leukocyte antigen (HLA) molecules in each donor–recipient pair undergoing stem-cell transplantation (SCT). Whole exome sequencing has previously demonstrated a large number of non-synonymous single nucleotide polymorphisms (SNP) present in HLA-matched recipients of SCT donors (GVH direction). The nucleotide sequence flanking each of these SNPs was obtained and the amino acid sequence determined. All the possible nonameric peptides incorporating the variant amino acid resulting from these SNPs were interrogated in silico for their likelihood to be presented by the HLA class I molecules using the Immune Epitope Database stabilized matrix method (SMM) and NetMHCpan algorithms. The SMM algorithm predicted that a median of 18,396 peptides weakly bound HLA class I molecules in individual SCT recipients, and 2,254 peptides displayed strong binding. A similar library of presented peptides was identified when the data were interrogated using the NetMHCpan algorithm. The bioinformatic algorithm presented here demonstrates that there may be a high level of mHA variation in HLA-matched individuals, constituting a HLA-specific alloreactivity potential.

Identification and Quantitation of Coding Variants and Isoforms of Pulmonary Surfactant Protein A

Pulmonary surfactant protein A (SP-A), a heterooligomer of SP-A1 and SP-A2, is an important regulator of innate immunity of the lung. Nonsynonymous single nucleotide variants of SP-A have been linked to respiratory diseases, but the expressed repertoire of SP-A protein in human airway has not been investigated. Here, we used parallel trypsin and Glu-C digestion, followed by LC-MS/MS, to obtain sequence coverage of common SP-A variants and isoform-determining peptides. We further developed a SDS-PAGE-based, multiple reaction monitoring (GeLC-MRM) assay for enrichment and targeted quantitation of total SP-A, the SP-A2 isoform, and the Gln223 and Lys223 variants of SP-A, from as little as one milliliter of bronchoalveolar lavage fluid. This assay identified individuals with the three genotypes at the 223 position of SP-A2: homozygous major (Gln223/Gln223), homozygous minor (Lys223/Lys223), or heterozygous (Gln223/Lys223). More generally, our studies demonstrate the challenges inherent in distinguishing highly homologous, copurifying protein isoforms by MS and show the applicability of MRM mass spectrometry for identification and quantitation of nonsynonymous single nucleotide variants and other proteoforms in airway lining fluid.

Integrating genomic, transcriptomic, and interactome data to improve Peptide and protein identification in shotgun proteomics

Mass spectrometry (MS)-based shotgun proteomics is an effective technology for global proteome profiling. The ultimate goal is to assign tandem MS spectra to peptides and subsequently infer proteins and their abundance. In addition to database searching and protein assembly algorithms, computational approaches have been developed to integrate genomic, transcriptomic, and interactome information to improve peptide and protein identification. Earlier efforts focus primarily on making databases more comprehensive using publicly available genomic and transcriptomic data. More recently, with the increasing affordability of the Next Generation Sequencing (NGS) technologies, personalized protein databases derived from sample-specific genomic and transcriptomic data have emerged as an attractive strategy. In addition, incorporating interactome data not only improves protein identification but also puts identified proteins into their functional context and thus facilitates data interpretation. In this paper, we survey the major integrative bioinformatics approaches that have been developed during the past decade and discuss their merits and demerits.

Large-scale mass spectrometric detection of variant peptides resulting from nonsynonymous nucleotide differences

Each individual carries thousands of nonsynonymous single nucleotide variants (nsSNVs) in their genome, each corresponding to a single amino acid polymorphism (SAP) in the encoded proteins. It is important to be able to directly detect and quantify these variations at the protein level to study post-transcriptional regulation, differential allelic expression, and other important biological processes. However, such variant peptides are not generally detected in standard proteomic analyses due to their absence from the generic databases that are employed for mass spectrometry searching. Here we extend previous work that demonstrated the use of customized SAP databases constructed from sample-matched RNA-Seq data. We collected deep-coverage RNA-Seq data from the Jurkat cell line, compiled the set of nsSNVs that are expressed, used this information to construct a customized SAP database, and searched it against deep-coverage shotgun MS data obtained from the same sample. This approach enabled the detection of 421 SAP peptides mapping to 395 nsSNVs. We compared these peptides to peptides identified from a large generic search database containing all known nsSNVs (dbSNP) and found that more than 70% of the SAP peptides from this dbSNP-derived search were not supported by the RNA-Seq data and thus are likely false positives. Next, we increased the SAP coverage from the RNA-Seq derived database by utilizing multiple protease digestions, thereby increasing variant detection to 695 SAP peptides mapping to 504 nsSNV sites. These detected SAP peptides corresponded to moderate to high abundance transcripts (30+ transcripts per million, TPM). The SAP peptides included 192 allelic pairs; the relative expression levels of the two alleles were evaluated for 51 of those pairs and were found to be comparable in all cases.

The repertoire of human tumor-associated epitopes--identification and selection of antigens and their application in clinical trials

In cancer patients, active immunotherapy has gained significant importance in recent years by implementation of novel substances into standard clinical care. These new drugs represent strategies which either use defined cancer associated antigens as vaccines or induce tumor-directed immune responses through generation of a general inflammatory state which has extensive autoinflammatory side effects by induction of autoreactive immune cells. Hence, the definition of suitable target antigens for immunotherapy remains a major challenge. These antigens should ideally be specific markers for individual tumors or should be at least structures overexpressed on the tumor as compared to normal cells. Recent approaches have defined algorithms and refined analytical methods for antigen identification and immunological validation that have already been evaluated in clinical studies. This article summarizes recent developments in tissue analysis on genome, transcriptome and HLA-ligandome levels and of antigen application in recent clinical vaccination trials.

The human leukocyte antigen-presented ligandome of B lymphocytes

Peptides presented by human leukocyte antigen (HLA) molecules on the cell surface play a crucial role in adaptive immunology, mediating the communication between T cells and antigen presenting cells. Knowledge of these peptides is of pivotal importance in fundamental studies of T cell action and in cellular immunotherapy and transplantation. In this paper we present the in-depth identification and relative quantification of 14,500 peptide ligands constituting the HLA ligandome of B cells. This large number of identified ligands provides general insight into the presented peptide repertoire and antigen presentation. Our uniquely large set of HLA ligands allowed us to characterize in detail the peptides constituting the ligandome in terms of relative abundance, peptide length distribution, physicochemical properties, binding affinity to the HLA molecule, and presence of post-translational modifications. The presented B-lymphocyte ligandome is shown to be a rich source of information by the presence of minor histocompatibility antigens, virus-derived epitopes, and post-translationally modified HLA ligands, and it can be a good starting point for solving a wealth of specific immunological questions. These HLA ligands can form the basis for reversed immunology approaches to identify T cell epitopes based not on in silico predictions but on the bona fide eluted HLA ligandome.