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BACKGROUND: Upon treatment with biopharmaceuticals, the immune system may produce anti-drug antibodies (ADA) that inhibit the therapy. Up to 40% of multiple sclerosis patients treated with interferon β (IFNβ) develop ADA, for which a genetic predisposition exists. Here, we present a genome-wide association study on ADA and predict the occurrence of antibodies in multiple sclerosis patients treated with different interferon β preparations.METHODS: We analyzed a large sample of 2757 genotyped and imputed patients from two cohorts (Sweden and Germany), split between a discovery and a replication dataset. Binding ADA (bADA) levels were measured by capture-ELISA, neutralizing ADA (nADA) titers using a bioassay. Genome-wide association analyses were conducted stratified by cohort and treatment preparation, followed by fixed-effects meta-analysis.RESULTS: Binding ADA levels and nADA titers were correlated and showed a significant heritability (47% and 50%, respectively). The risk factors differed strongly by treatment preparation: The top-associated and replicated variants for nADA presence were the HLA-associated variants rs77278603 in IFNβ-1a s.c.- (odds ratio (OR) = 3.55 (95% confidence interval = 2.81-4.48), p = 2.1 × 10-26) and rs28366299 in IFNβ-1b s.c.-treated patients (OR = 3.56 (2.69-4.72), p = 6.6 × 10-19). The rs77278603-correlated HLA haplotype DR15-DQ6 conferred risk specifically for IFNβ-1a s.c. (OR = 2.88 (2.29-3.61), p = 7.4 × 10-20) while DR3-DQ2 was protective (OR = 0.37 (0.27-0.52), p = 3.7 × 10-09). The haplotype DR4-DQ3 was the major risk haplotype for IFNβ-1b s.c. (OR = 7.35 (4.33-12.47), p = 1.5 × 10-13). These haplotypes exhibit large population-specific frequency differences. The best prediction models were achieved for ADA in IFNβ-1a s.c.-treated patients. Here, the prediction in the Swedish cohort showed AUC = 0.91 (0.85-0.95), sensitivity = 0.78, and specificity = 0.90; patients with the top 30% of genetic risk had, compared to patients in the bottom 30%, an OR = 73.9 (11.8-463.6, p = 4.4 × 10-6) of developing nADA. In the German cohort, the AUC of the same model was 0.83 (0.71-0.92), sensitivity = 0.80, specificity = 0.76, with an OR = 13.8 (3.0-63.3, p = 7.5 × 10-4).CONCLUSIONS: We identified several HLA-associated genetic risk factors for ADA against interferon β, which were specific for treatment preparations and population backgrounds. Genetic prediction models could robustly identify patients at risk for developing ADA and might be used for personalized therapy recommendations and stratified ADA screening in clinical practice. These analyses serve as a roadmap for genetic characterizations of ADA against other biopharmaceutical compounds.

Additional file 25. Treatment preparation-specific prediction of the presence of nADA in the replication data: performance of single models. Eight PRS, the top single GWAS variant, and the top HLA allele from the discovery stage. Covariates: sex, age, treatment preparation, treatment duration, titration site, and ancestry components. The plots show the area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (CI). Bonferroni = significant after Bonferroni correction for multiple testing; nominal = nominally significant (p

Additional file 13. Regional association plots of the top GWAS variants in the analysis across IFNβ preparations. Regional association plots of variants from the GWAS generated using LocusZoom v1.4 and the 1000 Genomes 1000G_Nov2014 EUR reference panel [72]. The color of dots indicates LD with the lead variant (pink). Gray dots represent signals with missing LD r2 values. If no LD information was present in the database on the top variant, LD with the variant showing the second-lowest p-value is indicated. The gray line indicates genome-wide significance. cM: centimorgan, chr: chromosome, Mb: mega base pairs.

Introduction Although intravenous immunoglobulin (IVIg) is commonly used in the clinic to treat various autoimmune and severe inflammatory diseases, the mode of action is not fully elucidated. This work investigates two proposed mechanisms: (1) the potential role of regulatory T‐cell epitopes (Tregitopes) from the constant domain of IgG in the immunosuppressive function of IVIg; and (2) a potential impact of IVIg on the ability of antigen presenting cells (APCs) to present peptides. Methods and Results Investigation of the HLA class II peptide repertoire from IVIg‐loaded dendritic cells (DCs) via MHC‐associated peptide proteomics (MAPPs) revealed that numerous IgG‐derived peptides were strongly presented along the antibody sequence. Surprisingly, Tregitopes 167 and 289 did not show efficient natural presentation although they both bound to HLA class II when directly loaded as “naked” peptides on human DCs. In addition, both Tregitopes could not reproduce the inhibitory effect of IVIg in a human in vitro T‐cell proliferation assay as well as in vivo in mice. MAPPs data demonstrate that presentation of peptides from several antigens remained unchanged even when competed with high doses of IVIg, in both human and mouse. Conclusion These data suggest that the effects mediated by IVIg are not caused by Tregitopes nor by impaired antigen presentation.

An important goal for personalized treatment is predicting response to a particular therapeutic. A drawback of biological treatment is immunogenicity and the development of antibodies directed against the drug [anti-drug antibodies (ADA)], which are associated with a poorer clinical outcome. Here we set out to identify a predictive biomarker that discriminates rheumatoid arthritis (RA) patients who are more likely to develop ADA in response to adalimumab, a human monoclonal antibody against tumor necrosis factor (TNF)α. By taking advantage of an immune-phenotyping platform, LEGENDScreen™, we measured the expression of 332 cell surface markers on B and T cells in a cross-sectional adalimumab-treated RA patient cohort with a defined ADA response. The analysis revealed seven differentially expressed markers (DEMs) between the ADA+ and ADA− patients. Validation of the DEMs in an independent prospective European cohort of adalimumab treated RA patients, revealed a significant and consistent reduced frequency of signal regulatory protein (SIRP)α/β-expressing memory B cells in ADA+ vs. ADA− RA patients. We also assessed the predictive value of SIRPα/β expression in a longitudinal RA cohort prior to the initiation of adalimumab treatment. We show that a frequency of < 9.4% of SIRPα/β-expressing memory B cells predicts patients that will develop ADA, and consequentially fail to respond to treatment, with a receiver operating characteristic (ROC) area under the curve (AUC) score of 0.92. Thus, measuring the frequency of SIRPα/β-expressing memory B cells in patients prior to adalimumab treatment may be clinically useful to identify a subgroup of active RA subjects who are going to develop an ADA response and not gain substantial clinical benefit from this treatment.

International audience; An important goal for personalized treatment is predicting response to a particular therapeutic. A drawback of biological treatment is immunogenicity and the development of antibodies directed against the drug [anti-drug antibodies (ADA)], which are associated with a poorer clinical outcome. Here we set out to identify a predictive biomarker that discriminates rheumatoid arthritis (RA) patients who are more likely to develop ADA in response to adalimumab, a human monoclonal antibody against tumor necrosis factor (TNF)alpha. By taking advantage of an immune-phenotyping platform, LEGENDScreen (TM), we measured the expression of 332 cell surface markers on B and T cells in a cross-sectional adalimumab-treated RA patient cohort with a defined ADA response. The analysis revealed seven differentially expressed markers (DEMs) between the ADA(+) and ADA(-) patients. Validation of the DEMs in an independent prospective European cohort of adalimumab treated RA patients, revealed a significant and consistent reduced frequency of signal regulatory protein (SIRP)alpha/beta-expressing memory B cells in ADA(+ )vs. ADA(-) RA patients. We also assessed the predictive value of SIRP alpha/beta-expression in a longitudinal RA cohort prior to the initiation of adalimumab treatment. We show that a frequency of

The chimeric antibodies anti-CD20 rituximab (Rtx) and anti-TNFα infliximab (Ifx) induce antidrug antibodies (ADAs) in many patients with inflammatory diseases. Because of the key role of CD4 T lymphocytes in the initiation of antibody responses, we localized the CD4 T cell epitopes of Rtx and Ifx. With the perspective to anticipate immunogenicity of therapeutic antibodies, identification of the CD4 T cell epitopes was performed using cells collected in healthy donors. Nine T cell epitopes were identified in the variable chains of both antibodies by deriving CD4 T cell lines raised against either Rtx or Ifx. The T cell epitopes often exhibited a good affinity for human leukocyte antigen (HLA)-DR molecules and were part of the peptides identified by MHC-associated peptide proteomics assay from HLA-DR molecules of dendritic cells (DCs) loaded with the antibodies. Two-third of the T cell epitopes identified from the healthy donors stimulated peripheral blood mononuclear cells from patients having developed ADAs against Rtx or Ifx and promoted the secretion of a diversity of cytokines. These data emphasize the predictive value of evaluating the T cell repertoire of healthy donors and the composition of peptides bound to HLA-DR of DCs to anticipate and prevent immunogenicity of therapeutic antibodies.

Die vorliegende Arbeit hatte zum Ziel, Einsichten in die Funktion der als CDw78 bezeichneten Membran-Mikrodomänen zu gewinnen. Bisher war lediglich bekannt, dass die CDw78 Mikrodomänen aus HLA und Tetraspan Molekülen zusammengesetzt sind und auf der Zelloberfläche von humanen antigenpräsentierenden Zellen exprimiert werden. Ungeklärt war hingegen unter anderem die Verwandtschaft von CDw78 Mikrodomänen zu Detergenz-resistenten Membran-Mikrodomänen (DRMs). Untersuchungen an DRMs und CDw78 Mikrodomänen mittels Saccharose-Gradienten sowie die Analyse der HLAgebundenen Peptide zeigten, dass die beiden Formen von Mikrodomänen zwar verschieden voneinander sind, jedoch durch schwache Interaktionen auch miteinander in Verbindung stehen können. Interessanterweise ergab die Analyse der HLA-Peptide, dass CDw78 Mikrodomänen im Gegensatz zur Gesamtheit der HLA-DR-präsentierten Peptide und im Gegensatz zu den DRMs nur ein sehr selektives Repertoire an Peptiden präsentieren. Dies konnte nicht nur für B Zellinien und Dendritische Zellen (DCs) gezeigt werden, sondern auch für PBMCs. Weiterführende Experimente zeigten, dass allein die Aminosäuresequenz über die Lokalisation eines Peptids in CDw78 Mikrodomänen bestimmen kann. In diesem Zusammenhang erwies sich der Austausch einer einzelnen Aminosäure als hinreichend für die Anreicherung eines definierten Peptids in CDw78 Mikrodomänen. Eine mögliche Erklärung für diesen Befund liefert die Beobachtung, dass HLA-DM, welches für seine Peptid-Editor Funktion bekannt ist, mit diesen Mikrodomänen assoziiert vorliegt. Der genaue Mechanismus bleibt jedoch noch zu ergründen. Bezüglich der Funktion der CDw78 Mikrodomänen für die Antigenpräsentation konnte gezeigt werden, dass CDw78 Mikrodomänen im Zentrum des supramolecular activation cluster (cSMAC) der immunologischen Synapse, die sich im Laufe einer spezifischen Interaktion von DCs und T Zellen ausbildet, stark angereichert werden. Ferner resultierte die Zerstörung der CDw78 Mikrodomänen auf B Zellen in der Unfähigkeit, T Zellen zu aktivieren, welche spezifisch für ein Antigen waren, welches in CDw78 angereichert wurde. Ebenso wenig waren T / B Hybridzellen, welche zwar mit HLA Molekülen transfiziert worden waren, jedoch keine CDw78 Mikrodomänen exprimierten, in der Lage diese T Zellen zu stimulieren, obwohl sie das entsprechende Peptid präsentierten. Zusammengenommen ergibt sich demnach, dass CDw78 Mikrodomänen zumindest für bestimmte Antigene eine essentielle Rolle während der Aktivierung von T Zellen einnehmen. Da sich die Hinweise häufen, dass Interferon-a (IFNa) während der Differenzierung und der Maturierung von DCs in vivo eine Rolle spielt, wurde dessen Einfluss auf die Expression von CDw78 analysiert. Es konnten jedoch bezüglich CDw78 keine signifikanten Unterschiede zwischen Differenzierung mit GM-CSF und IL-4 oder GM-CSF und IFNa festgestellt werden. Ebenso wenig zeigten sich Unterschiede für die Maturierung durch IFNa im Vergleich zu anderen Maturierungs-Stimuli, wie TNFa oder LPS.

Additional file 27. Comparison of allele frequencies for HLA-DRB1*15:01 and HLA-DRB1*04:01. The allele frequencies (AF) were queried from allelefrequencies.net on July 27th 2020 [48]. All four-digit European Silver and Gold populations with data on both HLA-DRB1*15:01 and HLA-DRB1*04:01 were used and populations with relative differences for both alleles are shown (i.e., with an AF above or below the average for one allele without the other allele being in the same group). Populations with an AF below the average for HLA-DRB1*15:01 and above the average for HLA-DRB1*04:01 are colored in green. Populations with an AF above the average for HLA-DRB1*15:01 and below the average for HLA-DRB1*04:01 are colored in magenta. In addition, the European populations with the highest or lowest AF for the respective allele (if not already present) as well as the largest German population and the Swedish SweHLA sample [47] are shown in gray.

Additional file 26. Treatment preparation-specific prediction of the presence of nADA in the replication data: comparison of top models. For each top model, the plots show either the AUC and its 95% CI or Nagelkerke’s pseudo-R2 and its 95% CI. Boxes show the prediction groups (replication data) and columns within each box the training data groups (discovery data). Bonferroni = significant after Bonferroni correction for multiple testing; nominal = nominally significant (p

Additional file 19. Forest plots of the top GWAS variants and HLA alleles in the analysis of IFNβ-1a s.c.-treated patients. Green: IFNβ-1a s.c., blue: IFNβ-1a i.m., orange: IFNβ-1b s.c., magenta: pooled discovery−/replication-stage analyses. D. = discovery, R. = replication, P. = pooled discovery + replication.

Additional file 3. New ADA measurements and design of the datasets for analyses. New ADA measurements in the Swedish KI and German TUM cohorts per treatment preparation and assignments of samples into the discovery and replication datasets. In the discovery and replication datasets, the first number indicates nADA and the second number bADA measurements. The distinction into negative and positive patients was made using nADA measurements.

Additional file 26. Treatment preparation-specific prediction of the presence of nADA in the replication data: comparison of top models. For each top model, the plots show either the AUC and its 95% CI or Nagelkerke’s pseudo-R2 and its 95% CI. Boxes show the prediction groups (replication data) and columns within each box the training data groups (discovery data). Bonferroni = significant after Bonferroni correction for multiple testing; nominal = nominally significant (p

Additional file 20. Regional association plots of the top GWAS variants in the analysis of IFNβ-1b s.c.-treated patients. Regional association plots of variants from the GWAS generated using LocusZoom v1.4 and the 1000 Genomes 1000G_Nov2014 EUR reference panel [72]. The color of dots indicates LD with the lead variant (pink). Gray dots represent signals with missing LD r2 values. If no LD information was present in the database on the top variant, LD with the variant showing the second-lowest p-value is indicated. The gray line indicates genome-wide significance. cM: centimorgan, chr: chromosome, Mb: mega base pairs.

Additional file 2. Additional details supporting the Methods section

Additional file 2. Additional details supporting the Methods section

Additional file 19. Forest plots of the top GWAS variants and HLA alleles in the analysis of IFNβ-1a s.c.-treated patients. Green: IFNβ-1a s.c., blue: IFNβ-1a i.m., orange: IFNβ-1b s.c., magenta: pooled discovery−/replication-stage analyses. D. = discovery, R. = replication, P. = pooled discovery + replication.