The Role of Class II Human Leukocyte Antigens (cII-HLAs) in Determining the Immunogenic Potential of Therapeutic Factor VIII Proteins in Hemophilia Patients: The "Gate Keeper" Hypothesis

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 .

Genetics of Factor VIII Inhibitor Development in Hemophilia Patients: Novel Statistical Approaches in the PATH Study

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.

Battle of the Blogosphere

This chapter examines how the multinational agricultural biotechnology corporation Monsanto has attempted to re-brand itself from a chemical company to a food company through the elaboration of a highly interlaced, multi-platform on-line media strategy. This image enhancement operation is a response to its many critics—from citizen-based groups in India and Mexico to prominent food security activists like Michael Pollan and Vandana Shiva. At the center of analysis is how Monsanto has used the trope of “sustainability” to craft a proactive profile that is responsive to the challenges that the planet is facing. Foregrounding the issue of environmental agency, the chapter provides an assessment of what kinds of environmental discourses the company privileges through its media operations, and how these have been produced as a means to combat those who have challenged Monsanto’s vision of food production and “responsible” environmental stewardship.

INSIDE INDUSTRY

Simulations Plus Announces Preliminary Success in Malaria Drug Design Project. xCELLigence System Evaluated in EU Project to Replace Animal Experiments in Cosmetics Industry. Progress in $100 Million Biodiesel and Commercial Fish Food Project. QIAGEN and Bio-X Center Open Shanghai Translational Medicine Lab. Index Ventures Launches First 150m Life Sciences Fund. Enzo Biochem Expands Distribution with Japan's Cosmo Bio. Maxwell Biotech Venture Fund Invests in Hepatitis B/D. ScinoPharm and NHRI Announce Jointly Developed Diabetes Drug. Calibr: A New Paradigm for Academic – Industry Cooperation. Biocon and Pfizer End Commercialization Agreement. Plandai Biotech Demonstrates Significance of Flavonoid & Polyphenol Bioavailability. Ceva Uses ProBioGen's AGE1.CR Cell Line for Viral Vaccine Production. Quintiles to Sign Memorandum of Agreement with Malaysia Biotechnology Corporation.

Iceland's Health Sector Database: A Significant Head Start in the Search for the Biological Grail or an Irreversible Error?

In December 1998, Iceland's Parliament, the Althing, passed the Act on a Health Sector Database (the Database Act or Act), a highly controversial law authorizing the development of a Health Sector Database (the Database) to collect genetic and medical information already contained in various locations around Iceland as part of Iceland's national health system. As a result of the Database Act, Iceland is the only country in the world with laws authorizing collection and storage of the genetic heritage of an entire population by a private biotechnology corporation with rights to exploit the data as a commercial commodity. Many databases now exist in Iceland as individual and segregated entities that contain detailed medical information about every Icelandic person, both living and dead, dating back to 1915 when the recording commenced.