ALBERT

Description: Intron-22-inversion patients express the entire Factor VIII (FVIII)-amino-acid sequence intracellularly as 2 non-secreted polypeptides and have a positive “intracellular (I)-FVIII-CRM” status. Mutations conferring a positive I-FVIII-CRM status are associated with low inhibitor risk and are pharmacogenetically relevant because inhibitor risk may be affected by the nature of the therapeutic FVIII-protein (tFVIII), the affinity of any tFVIII-derived foreign peptide (tFVIII-fp) for any HLA class-II isomer (HLA-II) comprising individual major histocompatibility complex (MHC) repertoires, and the stability of any tFVIII-fp/HLA-II complex. We hypothesize that mutations conferring a completely or substantially negative I-FVIII-CRM status are pharmacogenetically irrelevant because inhibitor risk is high with any tFVIII and individual MHC repertoire.

Description: The development of neutralizing antibodies-termed "inhibitors"-to infused therapeutic (t) factor VIII proteins (tFVIIIs) is the most serious obstacle to effective treatment of bleeding in Hemophilia A (HA) patients. As clinically significant FVIII immune responses are only initiated if dendritic cell (DC) cII-HLAs can present foreign tFVIII-derived peptides to naïve FVIII-specific T cells, we posit the "Gate Keeper" hypothesis in which the limiting determinant of inhibitor formation are patients' cII-HLA repertoires with the majority being individually distinct and each contributing slightly to the vast population level diversity of cII-HLAs. While cII-HLAs are critical at the cellular level for initiating immune responses, conflicting results from population studies have led some to describe their encoding HLA-II structural genes as weak determinants of inhibitor causation. Our main objective here is to test a hypothesis that gets at the heart of this disconnect between molecular-based expectations and population-level data by analyzing cII-HLA peptidomic data from DC-protein processing and presentation assays (PPPAs). The chief variable of DC-PPPA data is the peptide count, which we assume to be directly proportional to immunogenic potential (IP). Our working model is that inhibitor formation requires at minimum, in its initial stages, a complex between cII-HLAs and specific tFVIII-derived peptides. A testable null hypothesis under this thinking posits that a given cII-HLA allotype will have the same IP when exposed to several tFVIIIs. To test this hypothesis, we first performed model selection to determine the best set of predictor allotypes. To analyze the data, we employed a log-linear model where the peptide count is the dependent variable and allotype is a categorical independent variable consisting of 29 levels for 29 allotypes (8 DP, 10 DQ, and 11 DR allotypes). We used elastic net regression (ENR) to select the best set of allotype levels thus giving the best overall model consisting now of only four DR allotypes (Table 1). We then performed interaction analysis under the best-selected allotypes model in which we introduced as additional predictor variables, a tFVIII categorical variable consisting of five levels for five different tFVIIIs, namely full length (FL)-recombinant (r) FVIII (FL-rFVIII) ± von Willebrand Factor (VWF), B domain truncated (BDT)-rFVIII ± VWF, and plasma derived (pd) FVIII (pdFVIII) + VWF, and 12 interaction terms for the (4 - 1) × (5 - 1) possible interactions between the cII-HLA allotype and tFVIII variables. We found significant cII-HLA allotype × tFVIII interactions (Table 2). To get at the specific null hypothesis of interest, we examined within-allotype risk ratios (RRs) and their appropriately adjusted confidence intervals (CIs).1-4 It can be shown that an 84% CI is sufficient to achieve a significance level of α = 0.05 for the CI difference.2-4 Although there are 12 total interaction terms, per allotype there are only three possible CI comparisons on using the interaction term with the highest RR as a fixed reference. On constructing the adjusted CIs and correcting for multiple hypothesis testing,2 we found that two comparisons in Table 2 corresponded to significantly different RRs. We determined statistical power to detect a CI difference.1,3 As seen in Table 2, our study was extremely underpowered, which may explain why only two significant differences were found. Thus, at least for the two comparisons showing significant difference, we have refuted the null hypothesis of no difference across tFVIIIs for a given allotype, and have affirmed our working model that specific combinations of cII-HLAs and tFVIII-derived peptides are the triggering factor in inhibitor development.Schenker N, Gentleman J. On judging the significance of differences by examining the overlap between confidence intervals. Am Statistician. 2001; 55(3): 182-6.Julious S. Using confidence intervals around individual means to assess statistical significance between two means. Pharmaceut Statist. 2004; 3: 217-22.Maghsoodloo S, Huang C-Y. Comparing the overlapping of two independent confidence intervals with a single confidence interval for two normal population parameters. J Statist Plan & Infer. 2010; 140: 3295-305.Knol M, Pestman W, Grobbee D. The (mis)use of overlap of confidence intervals to assess effect modification. Eur J Epidemiol. 2011; 26(4): 253-4. Disclosures Hofmann: CSL Behring: Employment. Dinh:Haplomics Biotechnology Corporation: Employment, Equity Ownership. Escobar:Pfizer: Research Funding; Bayer, CSL Behring, Genentech, Hemabiologics, Kedrion, Novo Nordisk, Octapharma, Pfizer and Shire: Consultancy. Maraskovsky:CSL Behring: Employment. Howard:CSL Behring: Research Funding; Haplomics Biotechnology Corporation: Equity Ownership, Other: Chief Scientific Officer, Patents & Royalties: Patent applications and provisional patent applications .

Description: Introduction: Factor (F)VIII immunogenicity is the main obstacle to both successfully treating hemophilia and receiving FDA approval for new therapeutics. Since immune responses to allogeneically distinct proteins require T-cell activation and proliferation, at least one foreign peptide must be liberated from FVIII that can bind with high affinity to one or more of a patient's HLA-class-II (HLA-II) isomers. Due to the complex pathogenesis of inhibitor development, which involves numerous genetic variations and both treatment and product related variables, our ability to predict which patients will become alloimmunized to FVIII remains suboptimal. The role of FVIII glycosylation in inhibitor development appears to be an important, yet not well characterized, modifying influence; defining the mechanism has been complicated by the fact that some FVIII products are highly glycosylated with thousands of potential glycoforms. Here we describe studies of the spectrum of FVIII peptides eluted from HLA-II complexes and the impact of glycosylation on the proteolysis and/or binding of these peptides to a patient's individual HLA-II isomers, i.e. to further incorporate personalized medicine in hemophilia care. Methods: Immature monocyte-derived dendritic cells (DCs) from 12 unrelated donors were generated in vitro and matured in the presence of an equimolar pool of a full length (FL) and 4 B-domain deleted (BDD) rFVIII proteins free of VWF and other proteins. Following harvest and lysis, HLA-DP, -DQ, and -DR molecules were recovered using a specific immunoaffinity step. Peptides were then eluted from these complexes and sequenced by high resolution mass spectrometry (LC/MS/MS). The bound peptides were mapped to the FVIII reference sequence. The estimated binding affinity of FVIII peptides to the HLA-II alleles found in these donors were obtained from NetMHCIIpan and compared to the peptides observed to be bound. Results: The 12 DC donors express 29 distinct HLA-II isomers (6 DP, 11 DQ, 12 DR) from an overall HLA-II gene repertoire having the following alleles: DP: 01:03/02:01, 01:03/03:01, 01:03/04:01, 01:03/04:02, 02:01/01:01, 02:02/05:01 DQ: 01:01/05:03, 01:02/06:02, 01:02/06:09, 01:03/05:01, 01:03/06:03, 01:03/06:04, 02:01/02:01, 02:01/02:02, 03:01/03:02, 03:02/03:03, 05:01/03:01 DR: 01:01, 03:01, 04:01, 04:04, 07:01, 09:01, 11:01, 11:04, 13:01, 13:02, 14:01, 15:01 These 29 HLA-II isomers were found to have 77 bound peptides that covered 1,202 of the 2,332 total amino acid residues in the NCBI reference FL-FVIII protein, whose domain structure and consensus N-linked glycosylation (NLG) sites are shown in panel A of the Figure. The HLA-II bound peptides were mapped to the FVIII regions from which they derive (panel B). One of the 20 NLG sites known to be glycosylated and 3 of the 4 known non-glycosylated NLG sites were found to be in a bound peptide. A NetMHCIIpan analysis predicted that no FVIII nonamer containing a consensus NLG site binds strongly to any of the 12 DR isomers; but, it also clearly showed that more peptides bind with weak or strong affinity as peptide length increases (data not shown). Conclusion: Despite finding 3 of FVIII's 4 non-glycosylated consensus NLG sites in the tightly bound HLA-II/peptide complexes, only 1 of the 20 glycosylated NLG sites was found among these bound peptides. The near complete absence of N-linked glycans in the presented HLA-II bound peptides is compelling, but incomplete, evidence that glycosylation influences the immunogenicity of FVIII. Potential mechanisms may include effects on internalization, proteolysis and/or HLA-II binding. Further direct studies are required to determine if these post-translational modifications affect proteolysis and/or HLA-II binding; core peptides that can be proteolyzed and/or bound in the absence of glycosylation or with alternative glycan conformations may provide more evidence that glycosylation can modulate immunogenicity. We hypothesize that HLA-II bound and unbound FVIII peptides constitute a novel immune-response-related biomarker that will improve the accuracy of inhibitor risk prediction by allowing pertinent patient-specific pharmacogenomic inputs to be analyzed in a more biologically relevant manner. References: Karosiene et al. NetMHCIIpan-3.0, a pan-specific MHC-II prediction method including all 3 human MHC-II isotypes: HLA-DR, -DP and -DQ. Immunogenetics, 2013. Disclosures Hofmann: CSL Behring: Employment. Zollner:CSL Behring: Employment. Powell:CSL Behring: Employment. Maraskovsky:CSL Behring: Employment. Howard:Baxter: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria, Research Funding; Haplomics, Inc.: Patents & Royalties; CSL Behring: Consultancy, Honoraria, Research Funding.

Description: Background: Patients with the X-linked bleeding disorder hemophilia A have impaired blood clotting due to deficient or absent factor VIII (FVIII) coagulant activity. Bleeding can be managed by infusions of any of several plasma-derived (pd) or recombinant (r) therapeutic FVIII proteins (tFVIIIs). The efficacy of tFVIIIs can be eliminated, however, if neutralizing anti-tFVIII-antibodies called "inhibitors" develop. Since the development of inhibitors is T-helper-cell dependent, human leukocyte antigen (HLA)-class-II (HLAcII) molecules comprise an important early determinant. Accumulating evidence suggests that the presence of the FVIII chaperon protein, von Willebrand factor (VWF), in either pdFVIII or rFVIII concentrates, decreases the immunogenicity of these tFVIIIs by reducing their uptake by antigen presenting cells, especially dendritic cells (DCs). Objectives: Use a native (i.e., non-engineered) full-length (FL) tFVIII without ((−)) or with ((+)) pdVWF in DC-protein processing and presentation assays (PPPAs) followed by mass-spectrometric and peptide-proteomic analyses to identify and quantify the DP-, DQ-, and DR-bound/tFVIII-derived-peptides in individual HLAcII repertoires. Compare the number of peptides in the subset from any of the five globular domains (A1, A2, A3, C1 and C2) or three acidic-residue-rich connecting segments (a1, a2 and a3), which we collectively refer to as the non-B-domain (NBD) portion of a tFVIII, with the number of peptides from its non-globular "outrigger like" B-domain (BD) that contains ~80% of the N-linked glycans of a FL-FVIII molecule despite containing only 908 amino acid residues, i.e. slightly less than 40% of its 2,332 total residues. Methods: DC-PPPAs were performed using monocyte-derived (Mo)DCs obtained from 12 healthy blood donors. The tFVIII tested was a FL-rFVIII (Advate®) used (−) or (+) pdVWF (i.e., FL-rFVIII − pdVWF and FL-rFVIII + pdVWF) and the resulting data consists of counts of tFVIII-derived peptides presented on and extracted from HLAcII molecules. Difference of proportion tests were used to compare the effect of pdVWF as well as the NBD- and BD-regions of the FL tFVIII on the peptide counts. Results: FL-rFVIII − pdVWF yielded significantly more peptides (p

Description: The development of inhibitors against tFVIIIs represents a serious impediment to efficacious management of bleeding episodes in patients with hemophilia A (HA). It is therefore critical to understand the etiology of inhibitors to improve HA outcomes. Our group has recently presented evidence (Diego et al. Res Pract Thromb Haemost. 2019;3(Suppl. 1):50-51) on the validity of the Sequence Mismatch hypothesis, which posits that a sequence mismatch between the patient's endogenous FVIII sequence (i.e., due to the presence of a non-HA-causing non-synonymous single-nucleotide variation) and that underlying their tFVIII increases the risk that inhibitors will be induced during FVIII replacement therapy. For our study, 442 North American HA patients (237 Whites & 205 Blacks; 88% severely affected) were: 1) Immuno-Chip genotyped at ~167,000 SNPs in genes implicated in autoimmune disease risk; 2) Evaluated by Sanger DNA sequencing and assays for the recurrent intron (I)1- and I22-inversions to identify their F8-causal-mutations; and 3) Tested with the Nijmegen-modified Bethesda assay to determine their inhibitor status. The Immuno-Chip genotypes were used to construct a genetic-relationship matrix (GRM), and the F8 sequence data along with results from the I1- and I22-inversion (I22I) assays were used to construct a shared F8-mutation matrix (FMM). These matrices were used to estimate the heritable genetic and shared F8-mutation effects. Importantly, modeling a F8-mutation effect has the added advantage of accounting for the mutational heterogeneity in F8-mutations. We found that heritability and F8-mutation effects respectively accounted for 50% and 23% of the phenotypic variance in inhibitor (both p 〈 0.0001). Under the Sequence Mismatch hypothesis, it is assumed that tFVIII-derived peptides spanning the sequence mismatch must first be bound and presented on HLAcII molecules. In Diego et al. (2019), we reported a significant sequence mismatch effect but did not account for the effect of HLAcII binding. In a previous study of PATH data, it was shown using an area under the curve (AUC) estimate from receiver operator characteristic (ROC) curve analysis that HLAcII binding affinities estimated from the then-current version of NetMHCIIpan algorithm with respect to 15-mer peptides spanning sequence mismatches significantly predicted inhibitor status in a sample of 25 patients with the I22I mutation (Pandey et al. Nat Med. 2013;19(10): 1318-24). Here we adopt two strategies to extend this latter analysis to account for genetic relatedness and mutational heterogeneity. In the first strategy, we initially performed a Cholesky decomposition of the phenotypic covariance matrix expressed as a function of the GRM and FMM and their associated heritability and F8-mutation effect estimates, respectively. Following this, the derived Cholesky factor was used to decorrelate the HLAcII binding data, and then a bootstrap with replacement followed by ROC curve analysis each time (for 1,000 resampling's of the data) was used to generate an empirical distribution of the estimated AUC estimates. In the second strategy, we stratified the sample into the subset with the I22I mutation. We next performed a Cholesky decomposition of the GRM multiplied by the heritability and then used the resulting Cholesky factor to decorrelate the data for this subset. Finally, bootstrap with replacement followed by ROC curve analysis each time was used to generate an empirical distribution of the estimated AUC estimates. The p-values for both strategies are determined as the number of AUC estimates greater than that for the original transformed sample plus 1 divided by the total number of bootstraps plus 1. For the first time, we report results on the validity of the sequence mismatch hypothesis while modeling the effects of genetic relatedness, mutational heterogeneity, and HLAcII binding affinity. Disclosures Luu: Haplogenics Corporation: Employment. Chitlur:CSL-Behring: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda/Shire: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Research Funding; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bioveritiv/Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novo Nordisk Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Octapharma: Consultancy, Membership on an entity's Board of Directors or advisory committees. Dinh:Haplogenics Corporation: Employment. Mead:CSL Behring: Employment. Powell:Haplogenics: Membership on an entity's Board of Directors or advisory committees. Escobar:Novo Nordisk: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; National Hemophilia Foundation: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees. Howard:Haplogenics Corporation: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.

Description: Here we apply state-of-the-art statistical genetic approaches toward investigating the genetic architecture of factor VIII (FVIII) inhibitor (FEI) development in Hemophilia A (HA). A total of 442 North American HA patients (237 Whites and 205 Blacks; 88% severely affected) enrolled in the PATH Study were: 1) ImmunoChip genotyped at ~167,000 single nucleotide polymorphisms (SNPs) in genes previously implicated in autoimmune disease risk; 2) Evaluated by DNA sequencing and assays for the recurrent intron (I)1 and I22 inversions to identify their causative F8 mutations; and 3) Tested with the Bethesda assay to determine their FEI status. The ImmunoChip genotypes were used to construct a genetic relationship matrix (GRM), denoted by K, following our previously published method,1 and the F8 sequence data along with results from the I1 and I22 inversion assays were used to construct a shared F8-mutation matrix, denoted by F. We analyzed a dichotomous FEI variable under the statistical genetic threshold/liability model (a probit regression in the fixed effects) in conjunction with a variance components model for the FEI liability phenotypic covariance matrix, denoted by P, to model potentially important random effects. For the latter, we specifically assumed independent additive genetic, F8-mutation, and residual environmental random effects. By the independence assumption, the covariance matrix is then decomposable as a sum of the additive genetic (Va), F8-mutation (Vf), and residual environmental (Ve) variances respectively structured by K, F, and the identity matrix I. The variance component model is given as: P = K*Va + F*Vf + I*Ve. Heritability, denoted by h2, is defined as the ratio of Va to the total phenotypic variance (Vp): h2 = Va / Vp. We can further speak of the total heritability given as: h2t = h2r + h2f + h2snp, where the subscripts t, r, f, and snp respectively denote total, residual additive genetic, F8-mutation-specific, and SNP heritabilities. Using eigenstructure methods,2 we can compute power under a simpler model in which Va and Vf are combined as a single variance component. We computed power to detect genetic association as measured by SNP-specific heritability for a set of 403 SNPs in or near 14 candidate immune response genes previously implicated in FEI risk. To account for multiple hypothesis testing, power was computed at the Bonferroni-adjusted significance level of 0.05/403 = 1.2 × 10-4. Under the simplified model, we computed the statistical power to detect causal SNPs for our sample and study design for the sample FEI prevalences, denoted by Kp, for Whites (22.5%) and Blacks (45%), across a range of total heritabilities, h2t = 15%, 35%, and 55%, where the lattermost total heritability was observed for FEI liability in the current study (Figure 1). It should be noted that because the liability heritability is known to be biased upward, we applied the Dempster-Lerner correction to both the total and SNP-specific heritabilities.3 Close inspection of Figure 1 reveals that varying h2t from 15% to 35% to 55% results in slight decreases in power due to the decreasing ratio of the SNP-specific heritability to the total heritability. However, as seen in all three panels, the more important determinant of power is clearly the FEI prevalence in that the power curve for a Kp of 45% is associated with greater power than the power curve for a Kp of 22.5% across the range of total heritabilities examined. As seen in Figure 1, we have adequate power to detect SNP heritabilities as low as 5% and 6%, respectively, for a Kp of 45% and 22.5%. As noted above, we observed a FEI liability total heritability of 55% consisting of a 47% residual additive genetic heritability (p = 0.019) and 8% F8-mutation specific heritability (p = 0.005). This is the first study to use a GRM based on genotype data and a shared causal F8 mutation matrix to model additive genetic and F8-mutation specific effects.Almeida M, Peralta J, Farook V, …, Blangero J. Pedigree-based random effect tests to screen gene pathways. BMC Proc. 2014; 8(Suppl 1 Genetic Analysis Workshop): S100.Blangero J, Diego VP, Dyer T, …, Göring H. A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees. Adv Genetics. 2013; 81: 1-31.Glahn D, Williams J, McKay D, …, Blangero J. Discovering schizophrenia endophenotypes in randomly ascertained pedigrees. Biol Psychiatry. 2015; 77(1): 75-83. Disclosures Chitlur: Baxter, Bayer, Biogen Idec, and Pfizer: Honoraria; Novo Nordisk Inc: Consultancy. Dinh:Haplomics Biotechnology Corporation: Employment, Equity Ownership. Howard:Haplomics Biotechnology Corporation: Equity Ownership, Other: Chief Scientific Officer, Patents & Royalties: Patent applications and provisional patent applications ; CSL Behring: Research Funding.

Description: Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time-mean sea level were evaluated using the process-based climate model data and methods presented in the United Nations Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Regional surge and wave solutions extending from 1980 to 2100 were generated using  ∼  12 km resolution surge (Nucleus for European Modelling of the Ocean – NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled ( ∼  12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980 to 2010, enabling a quantitative assessment of model skill. Simulated historical sea-surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data, respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the Representative Concentration Pathway (RCP)4.5 (8.5) scenarios. Trends in surge and significant wave height 2-year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of  ∼  84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.