Associations of Genes for Killer Cell Immunoglobulin-Like Receptors and Their Human Leukocyte Antigen-A/B/C Ligands with Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is an immune-mediated disease with a genetic component. The multifactorial pathophysiology is not clear and there is still no pharmacotherapy to slow the growth of aneurysms. The signal integration of cell-surface KIRs (killer cell immunoglobulin-like receptors) with HLA (ligands, human leukocyte class I antigen molecules) modulates the activity of natural killer immune cells. The genetic diversity of the KIR/HLA system is associated with the risk of immune disorders. This study was a multivariate analysis of the association between genetic variants of KIRs, HLA ligands, clinical data and AAA formation. Genotyping was performed by single polymerase chain reaction with sequence-specific primers using commercial assays. Patients with HLA-A-Bw4 have a larger aneurysm by an average of 4 mm (p = 0.008). We observed a relationship between aneurysm diameter and BMI in patients with AAA and co-existing CAD; its shape was determined by the presence of HLA-A-Bw4. There was also a nearly 10% difference in KIR3DL1 allele frequency between the study and control groups. High expression of the cell surface receptor KIR3DL1 may protect, to some extent, against AAA. The presence of HLA-A-Bw4 may affect the rate of aneurysm growth and represents a potential regional pathogenetic risk of autoimmune injury to the aneurysmal aorta.

KIR-HLA Interactions Lack Clinical Utility in Matched Unrelated Donor Transplantation for AML: An Analysis of the CIBMTR and DRST Registries

Background: The interaction between donor killer immunoglobulin-like receptor (KIR) and recipient HLA has been postulated to enhance the graft-versus-leukemia effect in allogeneic hematopoietic cell transplantation (HCT) for acute myeloid leukemia (AML). Historically, analyses of individual interactions between single KIR and their respective HLA ligands have yielded conflicting findings, and the clinical importance of these interactions in the matched unrelated donor (MUD) setting remains controversial. Here, we applied a systematic approach, studying both a wide range of KIR and class I HLA interactions at the single-receptor level as well as the most prevalent KIR genotypes in a large cohort of AML patients undergoing MUD transplantation. Methods: We included adult AML patients in complete remission transplanted from an 8/8-HLA MUD between 2010 and 2016 and reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). Donor-KIR and respective recipient-HLA ligand interactions were assessed in multivariable Cox proportional hazard models for standard transplantation outcomes. To account for the compound effect of simultaneous KIR/HLA interactions, we applied a combinatorial approach to identify aggregate KIR genotypes based on combinations of individual KIR genes. The most frequently observed donor-KIR genotypes, in combination with recipient ligands, were evaluated for association with relapse using multivariable regression. Those associated (p < 0.01) with relapse risk were evaluated for differential relapse in a DRST (German stem-cell registry)/Collaborative Biobank cohort of donors/patients with similar inclusion criteria. Results: A total of 2,036 transplantations from the CIBMTR were included. Most patients were treated in first complete remission (78%) and received myeloablative conditioning (59%). We first studied eight known interactions between donor KIR and their respective HLA ligands (Figure A). Only donor-KIR-2DL2+/recipient-HLA-C1+ was associated with reduced relapse (compared to donor-KIR-2DL2-/recipient-HLA-C1+, hazard ratio [HR] 0.80 [95% confidence interval 0.67-0.94], p=0.008). However, no difference was found when comparing HLA-C group pairs among KIR-2DL2+ recipients, suggesting this finding is confounded by co-occurrence of other receptors. There are hundreds of possible KIR gene combinations (i.e. genotypes), which are typically clustered into two primary haplotypes, A and B. To study the cumulative effect of donor KIR, we investigated nine prevalent KIR genotypes (Figure B) and identified three significantly associated with relapse risk. (1) Donor KIR genotype 5 in all recipients irrespective of their HLA (Figure C, n = 138/2,036) and (2) genotype 3 in HLA-Bw4/x recipients (Figure D, n = 51/1,198) had significantly decreased relapse risk (HR 0.53 [0.37-0.78], p=0.002 and 0.34 [0.15-0.75], p=0.008, respectively). (3) KIR genotype 2 was associated with greater relapse in HLA-C1-homozygous recipients (Figure E, n = 87/836, HR 1.62 [1.14-2.30], p=0.007). These findings were not confirmed in the external European dataset (n = 796, Figure 1C-E); however, this cohort differed in ways that might affect the importance of KIRs, such as the higher frequency of reduced intensity conditioning (74% vs. 41%) and in-vivo T-cell depletion (79% vs. 37%). Conclusion: Our systematic investigation in two large AML cohorts receiving MUD allogenic HCT did not validate any association between individual KIR-HLA interactions and clinical outcomes. A combinatorial approach identified combinations potentially protective against relapse, however these could not be confirmed in a second dataset. Overall, our findings do not support KIR-informed donor selection using the approaches outlined here. Figure 1 Figure 1. Disclosures Shouval: Medexus: Consultancy. Kroeger: AOP Pharma: Honoraria; Gilead/Kite: Honoraria; Riemser: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Jazz: Honoraria, Research Funding; Sanofi: Honoraria; Neovii: Honoraria, Research Funding; Novartis: Honoraria. Horowitz: Daiicho Sankyo: Research Funding; Allovir: Consultancy; Miltenyi Biotech: Research Funding; Medac: Research Funding; Kite/Gilead: Research Funding; Genentech: Research Funding; Jazz Pharmaceuticals: Research Funding; Janssen: Research Funding; Kiadis: Research Funding; CSL Behring: Research Funding; Gamida Cell: Research Funding; bluebird bio: Research Funding; Bristol-Myers Squibb: Research Funding; Amgen: Research Funding; Astellas: Research Funding; Chimerix: Research Funding; GlaxoSmithKline: Research Funding; Novartis: Research Funding; Magenta: Consultancy, Research Funding; Actinium: Research Funding; Mesoblast: Research Funding; Omeros: Research Funding; Orca Biosystems: Research Funding; Pfizer, Inc: Research Funding; Pharmacyclics: Research Funding; Regeneron: Research Funding; Sanofi: Research Funding; Seattle Genetics: Research Funding; Shire: Research Funding; Sobi: Research Funding; Stemcyte: Research Funding; Takeda: Research Funding; Tscan: Research Funding; Vertex: Research Funding; Vor Biopharma: Research Funding; Xenikos: Research Funding. Malmberg: Merck: Research Funding; Vycellix: Consultancy; Fate Therapeutics: Consultancy, Research Funding. Miller: Sanofi: Membership on an entity's Board of Directors or advisory committees; Magenta: Membership on an entity's Board of Directors or advisory committees; ONK Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Vycellix: Consultancy; GT Biopharma: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics, Inc: Consultancy, Patents & Royalties, Research Funding; Wugen: Membership on an entity's Board of Directors or advisory committees. Mohty: Sanofi: Honoraria, Research Funding; Pfizer: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Jazz: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Gilead: Honoraria; Celgene: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria; Astellas: Honoraria; Amgen: Honoraria; Adaptive Biotechnologies: Honoraria. Romee: Crispr Therapeutics: Research Funding; Glycostem: Membership on an entity's Board of Directors or advisory committees. Schetelig: Roche: Honoraria, Other: lecture fees; Novartis: Honoraria, Other: lecture fees; BMS: Honoraria, Other: lecture fees; Abbvie: Honoraria, Other: lecture fees; AstraZeneca: Honoraria, Other: lecture fees; Gilead: Honoraria, Other: lecture fees; Janssen: Honoraria, Other: lecture fees . Weisdorf: Fate Therapeutics: Research Funding; Incyte: Research Funding. Koreth: Biolojic Design: Other: Scientific Advisory Board; Mallinckrodt: Other: Scientific Advisory Board; Cugene: Other: Scientific Advisory Board; Moderna: Consultancy; Amgen: Consultancy; EMD Serono/Merck: Consultancy; Gentibio Inc.: Consultancy; Miltenyi Biotec: Research Funding; BMS: Research Funding; Clinigen Labs: Research Funding; Regeneron: Research Funding; Equillium: Research Funding.

A facile immunopeptidomics workflow for capturing the HLA-I ligandome with PEAKS XPro

Identifying antigens displayed specifically on tumour cell surfaces by human leukocyte antigen (HLA) proteins is important for the development of immunotherapies and cancer vaccines. The difficulty in capturing an HLA ligandome stems from the fact that many HLA ligands are derived from splicing events or contain mutations, hindering their identification in a standard database search. To address this challenge, we developed an immunopeptidomics workflow with PEAKS XPro that uses de novo sequencing to uncover such peptides and identifies mutations for neoantigen discovery. We demonstrate the utility of this workflow by re-analyzing HLA-I ligandome datasets and reveal a vast diversity in peptide sequences among clones derived from a colorectal cancer tumour. Over 8000 peptides predicted to bind HLA-I molecules were identified by de novo sequencing only (not found in the UniProt database) and make up over 50% of identified peptides from each sample. Lastly, tumour-specific mutations and consensus sequence motif characteristics are defined. This workflow is widely applicable to any immunopeptidomic mass spectrometry dataset and does not require custom database generation for neoantigen discovery.

Coexistence of inhibitory and activating killer-cell immunoglobulin-like receptors to the same cognate HLA-C2 and Bw4 ligands confer breast cancer risk

Human leukocyte antigen (HLA) class I-specific killer-cell immunoglobulin-like receptors (KIR) regulate natural killer (NK) cell function in eliminating malignancy. Breast cancer (BC) patients exhibit reduced NK-cytotoxicity in peripheral blood. To test the hypothesis that certain KIR-HLA combinations impairing NK-cytotoxicity predispose to BC risk, we analyzed KIR and HLA polymorphisms in 162 women with BC and 278 controls. KIR-Bx genotypes increased significantly in BC than controls (83.3% vs. 71.9%, OR 1.95), and the increase was more pronounced in advanced-cancer (OR 5.3). No difference was observed with inhibitory KIR (iKIR) and HLA-ligand combinations. The activating KIR (aKIR) and HLA-ligand combinations, 2DS1 + C2 (OR 2.98) and 3DS1 + Bw4 (OR 2.6), were significantly increased in advanced-BC. All patients with advanced-cancer carrying 2DS1 + C2 or 3DS1 + Bw4 also have their iKIR counterparts 2DL1 and 3DL1, respectively. Contrarily, the 2DL1 + C2 and 3DL1 + Bw4 pairs without their aKIR counterparts are significantly higher in controls. These data suggest that NK cells expressing iKIR to the cognate HLA-ligands in the absence of putative aKIR counterpart are instrumental in antitumor response. These data provide a new framework for improving the utility of genetic risk scores for individualized surveillance.

TANTIGEN 2.0: a knowledge base of tumor T cell antigens and epitopes

We previously developed TANTIGEN, a comprehensive online database cataloging more than 1000 T cell epitopes and HLA ligands from 292 tumor antigens. In TANTIGEN 2.0, we significantly expanded coverage in both immune response targets (T cell epitopes and HLA ligands) and tumor antigens. It catalogs 4,296 antigen variants from 403 unique tumor antigens and more than 1500 T cell epitopes and HLA ligands. We also included neoantigens, a class of tumor antigens generated through mutations resulting in new amino acid sequences in tumor antigens. TANTIGEN 2.0 contains validated TCR sequences specific for cognate T cell epitopes and tumor antigen gene/mRNA/protein expression information in major human cancers extracted by Human Pathology Atlas. TANTIGEN 2.0 is a rich data resource for tumor antigens and their associated epitopes and neoepitopes. It hosts a set of tailored data analytics tools tightly integrated with the data to form meaningful analysis workflows. It is freely available at http://projects.met-hilab.org/tadb.

HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy

BackgroundThe human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level.MethodsHuman tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing.ResultsThe initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference.ConclusionGiven that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org.

HLA binding of self-peptides is biased towards proteins with specific molecular functions

Human leukocyte antigen (HLA) is highly polymorphic and plays a key role in guiding adaptive immune responses by presenting foreign and self peptides to T cells. Each HLA variant selects a minor fraction of peptides that match a certain motif required for optimal interaction with the peptide-binding groove. These restriction rules define the landscape of peptides presented to T cells. Given these limitations, one might suggest that the choice of peptides presented by HLA is non-random and there is preferential presentation of an array of peptides that is optimal for distinguishing self and foreign proteins. In this study we explore these preferences with a comparative analysis of self peptides enriched and depleted in HLA ligands. We show that HLAs exhibit preferences towards presenting peptides from certain proteins while disfavoring others with specific functions, and highlight differences between various HLA genes and alleles in those preferences. We link those differences to HLA anchor residue propensities and amino acid composition of preferentially presented proteins. The set of proteins that peptides presented by a given HLA are most likely to be derived from can be used to distinguish between class I and class II HLAs and HLA alleles. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Finally, we show that the reported self peptidome preferences of distinct HLA variants can be compensated by combinations of HLA-A/HLA-B and HLA-A/HLA-C alleles in frequent haplotypes.

The Role of HLA and KIR Immunogenetics in BK Virus Infection after Kidney Transplantation

BK virus (BKV) is a polyomavirus with high seroprevalence in the general population with an unremarkable clinical presentation in healthy people, but a potential for causing serious complications in immunosuppressed transplanted patients. Reactivation or primary infection in kidney allograft recipients may lead to allograft dysfunction and subsequent loss. Currently, there is no widely accepted specific treatment for BKV infection and reduction of immunosuppressive therapy is the mainstay therapy. Given this and the sequential appearance of viruria-viremia-nephropathy, screening and early detection are of utmost importance. There are numerous risk factors associated with BKV infection including genetic factors, among them human leukocyte antigens (HLA) and killer cell immunoglobulin-like receptors (KIR) alleles have been shown to be the strongest so far. Identification of patients at risk for BKV infection would be useful in prevention or early action to reduce morbidity and progression to frank nephropathy. Assessment of risk involving HLA ligands and KIR genotyping of recipients in the pre-transplant or early post-transplant period might be useful in clinical practice. This review summarizes current knowledge of the association between HLA, KIR and BKV infection and potential future directions of research, which might lead to optimal utilization of these genetic markers.

Association of Inhibitory Killer Cell Immunoglobulin-like Receptor Ligands With Higher Plasmodium falciparum Parasite Prevalence

Killer cell immunoglobulin-like receptors (KIRs) and their HLA ligands influence the outcome of many infectious diseases. We analyzed the relationship of compound KIR-HLA genotypes with risk of Plasmodium falciparum infection in a longitudinal cohort of 890 Ugandan individuals. We found that presence of HLA-C2 and HLA-Bw4, ligands for inhibitory KIR2DL1 and KIR3DL1, respectively, increased the likelihood of P. falciparum parasitemia in an additive manner. Individuals homozygous for HLA-C2, which mediates strong inhibition via KIR2DL1, had the highest odds of parasitemia, HLA-C1/C2 heterozygotes had intermediate odds, and individuals homozygous for HLA-C1, which mediates weaker inhibition through KIR2DL2/3, had the lowest odds of parasitemia. In addition, higher surface expression of HLA-C, the ligand for inhibitory KIR2DL1/2/3, was associated with a higher likelihood of parasitemia. Together these data indicate that stronger KIR-mediated inhibition confers a higher risk of P. falciparum parasitemia and suggest that KIR-expressing effector cells play a role in mediating antiparasite immunity.

HLA Alleles and KIR Genes in Romanian Patients with Chronic Hepatitis C

Background and Aims: The role of natural killer (NK) cells in the defense against hepatitis C virus (HCV) infection involve both innate and adaptive immunity. NK cells express a large panel of inhibitory and activating receptors who bind human leukocyte antigen (HLA) class I receptors. Killer cell immunoglobulin-like receptors (KIRs) are the most polymorphic of these receptors being encoded by genes distributed differently in unrelated individuals. The aim of this study was to look at the immune response in chronic HCV patients by assessing NK-KIR genes and their corresponding HLA ligands. Methods: We genotyped 127 chronically HCV-infected patients and 130 non-infected healthy individuals for both KIR genes and their HLA ligands. The HLA-A, HLA-B, HLA-C genotypes were analyzed using polymerase chain reaction high-resolution typing. Results: KIR2DL3, KIR2DL5, KIR2DS4 norm, KIR3DL3, KIR2DP1, KIR3DP1 genes were significantly increased in the HCV group compared to healthy individual. Analysis of various HLA haplotypes revealed different HLA alleles associated with increased susceptibility to HCV infection. Thus, HLA A*23:01 was more frequent in the patients’ group than in the controls (p=0.030). At the same time HLA B*44:02 and C*04:02 were significantly elevated in HCV-positive patients (p=0.008 and respectively p= 0.007). Conclusions: These results suggest that the expression of KIR2DL3, KIR2DL5, KIR2DS4 norm, KIR3DL3 genes and the association with HLA alleles such as HLA A*23:01, B*44:02, C*04:02 may increase the patient susceptibility to chronic HCV infection.