Summary
We investigated the influence of IL-7 receptor α chain (IL-7Rα) gene haplotypes in donors on the outcome of haematopoietic cell transplantation (HCT). Unlike the association between single donor SNPs and HCT outcome found previously, only trends towards association were found here, due to “dilution” of SNPs into haplotypes.
Introduction
Interleukin-7 (IL-7) is essential for thymic T cell development (Hong et al., 2012) and peripheral T cell maintenance (Carrette & Surh, 2012). The IL-7 receptor (IL-7R) is a heterodimer consisting of the α-chain (IL-7Rα/CD127) and the common γ chain (CD132). IL-7Rα also combines with the receptor of thymic stromal lymphopoietin (TSLP), a cytokine with diverse effects, including thymic development of Foxp3+ regulatory T cells (Tregs) (Watanabe et al., 2005), peripheral differentiation of T helper (Th) 2 cells (Ito et al., 2005) and stimulation of TNF production by dendritic cells (DCs) (Soumelis et al., 2002). The IL-7Rα gene is polymorphic and has been shown to be associated with risk of developing immune disorders such as multiple sclerosis (Broux et al., 2010; Gregory et al., 2007; Lundmark et al., 2007) and sarcoid inflammation (Heron et al., 2009), and with poor outcome after haematopoietic stem cell transplantation (HCT) (Shamim et al., 2013).
HCT is a treatment for severe haematologic malignancies as well as a number of benign diseases including severe aplastic anemia and immunodeficiences. However, recipients suffer from a prolonged post-transplant immune deficiency resulting in significant morbidity and mortality and a significant number of patients experience a relapse of leukemia after the transplant (Socie et al., 1999). Previous studies indicated that certain IL-7Rα single nucleotide polymorphisms (SNPs), when present in the HCT donor, are associated with a worse outcome after transplantation. More specifically, rs1494555GG and rs1494558TT donor genotypes are associated with acute and chronic graft versus host disease (GvHD) (Shamim et al., 2006; Shamim et al., 2013), while rs6897932T has been associated with relapse of leukaemia after HCT (Shamim et al., 2013). These results indicate a role of the IL-7 pathway and IL-7Rα polymorphisms in the outcome after HCT. These findings have suggested that selection of donors based on IL-7Rα genotyping may lead to an improved survival. In addition to single SNPs, genotyping of IL-7Rα can also be performed by analyzing the four common haplotypes, thus providing information about all the SNPs in these haplotypes in one assay (Teutsch et al., 2003). Therefore, this study aims at validating the previous studies that were limited to single SNPs, by analyzing the complete IL-7Rα haplotype of donors.
Materials and methods
Study population
A total of 591 donor/recipient pairs receiving a bone marrow (BM) or growth factor-mobilized peripheral blood stem cell (PBSC) transplant following a myeloablative conditioning regimen between 1988 and 2004 were included. All HCT were facilitated through the National Marrow Donor Program (NMDP) with clinical outcome data collected through the Center for International Blood and Marrow Transplant Research (CIBMTR). For this study, patients from 78 centers are included. The study population is described in detail by (Shamim et al., 2013) and in Table 1.
Table 1.
Study population
| Variable | n (%) |
|---|---|
| Recipient age, median (range), years | 38 (18–62) |
| Donor age, median (range), years | 36 (18–59) |
| Recipient age at transplant | |
| 18–19 years | 18 (3) |
| 20–29 years | 126 (21) |
| 30–39 years | 176 (30) |
| 40–49 years | 202 (34) |
| 50 and older | 69 (12) |
| Males amongst recipients | 344 (58) |
| Disease at transplant | |
| AML | 111 (19) |
| ALL | 76 (13) |
| CML | 374 (63) |
| MDS | 30 (5) |
| Disease status at transplant | |
| Early | 427 (72) |
| Intermediate | 164 (28) |
| GVHD prophylaxis | |
| FK506±MTX±MMF±Steroids±other | 147 (25) |
| CsA + MTX ± other | 415 (70) |
| CsA ± other (No MTX) | 25 (4) |
| MTX ± other (No CsA) | 4 (1) |
| Graft type | |
| Marrow | 519 (88) |
| Peripheral blood | 72 (12) |
| Donor/Recipient sex match | |
| Male/Male | 239 (40) |
| Male/Female | 143 (24) |
| Female/Male | 105 (18) |
| Female/Female | 104 (18) |
| Donor/Recipient CMV match | |
| Negative/Negative | 233 (39) |
| Negative/Positive | 159 (27) |
| Positive/Negative | 85 (14) |
| Positive/Positive | 94 (16) |
| Unknown | 20 (3) |
| Donor Haplotype GTG (Hap1) | |
| GTG/GTG | 58 (10) |
| one GTG | 242 (41) |
| no GTG | 275 (47) |
| not typed | 16 (3) |
| Donor Haplotype GTA (Hap2) | |
| GTA/GTA | 45 (8) |
| one GTA | 225 (38) |
| no GTA | 305 (52) |
| not typed | 16 (3) |
| Donor Haplotype TTA (Hap3) | |
| TTA/TTA | 6 (1) |
| one TTA | 135 (23) |
| no TTA | 434 (73) |
| not typed | 16 (3) |
| Donor Haplotype GCA (Hap4) | |
| GCA/GCA | 47 (8) |
| one GCA | 236 (40) |
| no GCA | 292 (49) |
| not typed | 16 (3) |
| Donor full haplotype distribution | |
| GTG/GTG | 58 (10) |
| GTG/GTA | 95 (16) |
| GTG/TTA | 48 (8) |
| GTG/GCA | 99 (17) |
| GTA/GTA | 45 (8) |
| GTA/TTA | 40 (7) |
| GTA/GCA | 90 (15) |
| TTA/TTA | 6 (1) |
| TTA/GCA | 47 (8) |
| GCA/GCA | 47 (8) |
| Not typed | 16 (3) |
| Median follow-up of survivors, mo (range) | 93 (10–207) |
Ethics statement
Observational studies conducted by the CIBMTR are performed in compliance with the privacy rule (HIPAA) as a Public Health Authority and in compliance with all applicable federal regulations pertaining to the protection of human research participants as determined by continuous review of the Institutional Review Boards of the NMDP and the Medical College of Wisconsin. Surviving patients who did not provide signed, informed consent to allow analysis of their clinical data or HLA typing of stored NMDP Research Repository samples were excluded. All surviving recipients included in this analysis were retrospectively contacted and provided informed consent for participation in the NMDP/CIBMTR research program. To adjust for the potential bias introduced by exclusion of non-consenting surviving patients, a modeling process randomly excluded the same percentage of deceased patients using a biased coin randomization with exclusion probabilities based on characteristics associated with not providing consent for use of the data in survivors.(Farag et al., 2006) This procedure is standard for CIBMTR analyses to avoid bias from the retrospective consent process.
Endpoints
The outcomes analysed in this study are non-relapse mortality (NRM), relapse, acute and chronic GvHD, disease-free survival (DFS) and overall survival (OS). Relapse was defined as leukaemia recurrence, with death in continuous remission as a competing risk. NRM was death in the absence of relapse, with relapse as a competing risk. Acute GvHD was defined as development of grades 2–4 and grades 3–4 according to the Glucksberg criteria, with death as a competing risk (Glucksberg et al., 1974). Chronic GvHD was diagnosed following standard definitions, with death as a competing risk (Shulman et al., 1980). DFS was defined as survival in complete remission after HCT, with relapse as a competing risk. For OS, death from any cause was considered an event. All living patients were censored at last follow-up.
Detection of IL-7Rα haplotypes
HCT donors were genotyped for the IL-7Rα locus using a restriction fragment length polymorphism (RFLP) procedure, as previously described (Broux et al., 2010; Teutsch et al., 2003). Briefly, a fragment of the IL-7Rα gene promoter region, including three tagging SNPs (rs7718919, rs11567685 and rs11567686), was PCR amplified from genomic DNA of donors. This fragment was then cut by the restriction enzymes HphI (Fermentas, Burlington, Ontario, Canada) and PstI (New England Biolabs, Ipswich, MA, USA) to obtain banding patterns specific for each of the four common haplotypes. To verify our genotyping assay, 4 to 6 samples per haplotype were sequenced using an ABI automated DNA sequencer (PerkinElmer, Waltham, MA, USA) to validate our gel-based genotyping technique. For 16 donors, no genotype could be determined due to technical issues (indicated as “not typed” in Table 1).
Statistics
Patient, donor, disease and transplant-related factors were compared between groups using the Chi-square test for categorical variables and the Wilcoxon sample test for continuous variables. Patient factors included age, sex, Karnofsky score and cytomegalovirus (CMV) status. Donor factors included age, sex, CMV status and number of pregnancies. Disease factors included disease and disease stage. Transplant-related factors included graft type, GvHD prophylaxis, use of anti-thymocyte globulin (ATG), donor/patient CMV match, donor/patient sex/match, graft type, time from diagnosis to transplant and year of transplant. Surviving patients were censored at the time of last contact.
Multivariate analysis used Cox’s proportional hazard model. All clinical variables were tested for proportional hazards assumptions. Factors violating the proportional hazards assumption were adjusted through stratification. A stepwise model building procedure was then used to select clinical risk factors for each outcome with a threshold of p≤0.05 for entering into the model. The main variable of IL-7Rα haplotypes was then tested by forcing it into the models. Due to multiple testing, p<0.01 was used to determine statistical significance for the main effect. The transplantation outcome analyses were performed using SAS version 9.2 (SAS Institute, Inc). The Hardy-Weinberg equilibrium tests were performed using R Package Genetics.
Results and discussion
IL-7Rα haplotype frequencies
For this study, 591 donors were genotyped for the four common IL-7Rα haplotypes, as described in Table 1. There were no deviations from Hardy-Weinberg equilibrium, and the haplotype frequencies correspond to those previously reported (Broux et al., 2010; Gregory et al., 2007; Hafler et al., 2007; Lundmark et al., 2007; Teutsch et al., 2003).
IL-7Rα haplotypes and HCT outcome
In order to identify an effect of donor IL-7Rα haplotypes on outcome after HCT, we performed both univariate and multivariate analyses. The outcome measures which were analyzed included survival, NRM, relapse, aGvHD and cGvHD. In contrast to single SNP associations found in previous studies using the same cohort of donors, we did not find an association of any haplotype combination or carriage of any haplotype with the outcome measures, either in a univariate analysis model (Table 2) or a multivariate model (Table 3) in this study. However, the trends that were observed, although they were not significant due to multiple testing and a low number of individuals or events in certain groups (0.01<p<0.05), were comparable to those found in the previous single SNP study (Shamim et al., 2013). Specifically, we found that Hap1 (containing rs1494555G and rs1494558T) was suggestive for association with acute and chronic GvHD, and that Hap2 (containing rs6897932T) was suggestive for association with relapse and aGvHD in the univariate analysis (Table 2). Hap2 was also suggestive for association with relapse in the multivariate analysis (Table 3). These results confirm our previous data, although significance was not reached in the present study.
Table 2.
Univariate analysis
| Univariate Probability of Outcomes for GTG (Hap1) distribution | |||||||
|---|---|---|---|---|---|---|---|
| Outcome | no GTG | one GTG | GTG/GTG | Pointwise p-value | |||
| n | Prob (95% CI) | n | Prob (95% CI) | n | Prob (95% CI) | ||
| Survival | 275 | 242 | 58 | 0.31a | |||
| @ 5 years | 45 (40–51) | 48 (41–54) | 49 (36–62) | 0.82 | |||
| NRM | 270 | 239 | 57 | ||||
| @ 5 years | 41 (35–47) | 40 (34–46) | 39 (27–52) | 0.96 | |||
| Relapse | 270 | 239 | 57 | ||||
| @ 5 years | 16 (12–21) | 16 (12–21) | 14 (6–24) | 0.90 | |||
| AGVHD II-IV | 272 | 239 | 58 | ||||
| @ 100 days | 51 (45–57) | 55 (49–61) | 69 (57–80) | 0.03 | |||
| AGVHD III-IV | 264 | 233 | 54 | ||||
| @ 100 days | 25 (20–30) | 23 (18–28) | 22 (12–34) | 0.86 | |||
| CGVHD II-IV | 273 | 241 | 56 | ||||
| @ 1 year | 50 (44–56) | 48 (42–55) | 68 (55–79) | 0.02 | |||
| Univariate Probability of Outcomes for GTA (Hap2) distribution | |||||||
|---|---|---|---|---|---|---|---|
| Outcome | no GTA | one GTA | GTA/GTA | Pointwise p-value | |||
| n | Prob (95% CI) | n | Prob (95% CI) | n | Prob (95% CI) | ||
| Survival | 305 | 225 | 45 | 0.47a | |||
| @ 5 years | 47 (42–53) | 46 (40–53) | 44 (30–59) | 0.93 | |||
| NRM | 300 | 223 | 43 | ||||
| @ 5 years | 42 (36–48) | 39 (33–45) | 38 (24–53) | 0.74 | |||
| Relapse | 300 | 223 | 43 | ||||
| @ 5 years | 12 (9–16) | 19 (15–25) | 24 (12–38) | 0.03 | |||
| AGVHD II-IV | 302 | 222 | 45 | ||||
| @ 100 days | 56 (50–61) | 55 (48–61) | 44 (30–59) | 0.36 | |||
| AGVHD III-IV | 292 | 215 | 44 | ||||
| @ 100 days | 25 (20–30) | 24 (19–30) | 14 (5–25) | 0.14 | |||
| CGVHD II-IV | 303 | 223 | 44 | ||||
| @ 1 year | 51 (45–57) | 52 (45–59) | 49 (34–64) | 0.92 | |||
| Univariate Probability of Outcomes for TTA (Hap3) distribution | |||||||
|---|---|---|---|---|---|---|---|
| Outcome | no TTA | one TTA | TTA/TTA | Pointwise p-value | |||
| n | Prob (95% CI) | n | Prob (95% CI) | n | Prob (95% CI) | ||
| Survival | 434 | 135 | 6 | 0.39a | |||
| @ 5 years | 46 (41–50) | 51 (43–59) | 33 (5–72) | 0.43 | |||
| NRM | 429 | 131 | 6 | ||||
| @ 5 years | 41 (36–45) | 39 (31–47) | 67 (28–95) | 0.37 | |||
| Relapse | 429 | 131 | 6 | ||||
| @ 5 years | 17 (14–21) | 13 (8–20) | 0 (.-.) | N/A | |||
| AGVHD II-IV | 429 | 134 | 6 | ||||
| @ 100 days | 56 (51–61) | 49 (40–57) | 67 (28–95) | 0.24 | |||
| AGVHD III-IV | 415 | 130 | 6 | ||||
| @ 100 days | 23 (19–27) | 25 (18–32) | 33 (5–72) | 0.83 | |||
| CGVHD II-IV | 430 | 134 | 6 | ||||
| @ 1 year | 52 (47–57) | 50 (41–58) | 33 (5–72) | 0.58 | |||
| Univariate Probability of Outcomes for GCA (Hap4) distribution | |||||||
|---|---|---|---|---|---|---|---|
| Outcome | no GCA | one GCA | GCA/GCA | Pointwise p-value | |||
| n | Prob (95% CI) | n | Prob (95% CI) | n | Prob (95% CI) | ||
| Survival | 292 | 236 | 47 | 0.62a | |||
| @ 5 years | 48 (42–54) | 47 (41–53) | 39 (25–54) | 0.53 | |||
| NRM | 286 | 233 | 47 | ||||
| @ 5 years | 39 (33–45) | 41 (34–47) | 47 (33–62) | 0.56 | |||
| Relapse | 286 | 233 | 47 | ||||
| @ 5 years | 18 (14–23) | 14 (10–19) | 13 (5–25) | 0.38 | |||
| AGVHD II-IV | 290 | 233 | 46 | ||||
| @ 100 days | 56 (50–61) | 53 (47–60) | 54 (40–68) | 0.87 | |||
| AGVHD III-IV | 280 | 226 | 45 | ||||
| @ 100 days | 22 (17–27) | 23 (18–29) | 33 (20–48) | 0.32 | |||
| CGVHD II-IV | 287 | 236 | 47 | ||||
| @ 1 year | 53 (47–58) | 51 (44–57) | 45 (31–59) | 0.59 | |||
Abbreviations: Prob = Probability (%); CI = Confidence interval
Log-rank p-value
Table 3.
Multivariate analysis
| Haplotype 1: GTG | no GTG | one GTG | GTG/GTG | Overall p-value |
|---|---|---|---|---|
|
| ||||
| Ref | HR (95% CI) | HR (95% CI) | ||
| Overall survivala | 1 | 0.91 (0.73–1.15) | 0.72 (0.49–1.06) | 0.24 |
| NRMb | 1 | 0.94 (0.73–1.23) | 0.79 (0.51–1.24) | 0.59 |
| Relapsec | 1 | 0.84 (0.54–1.30) | 0.61 (0.28–1.36) | 0.42 |
| aGVHD II-IVc | 1 | 1.07 (0.84–1.35) | 1.24 (0.87–1.76) | 0.48 |
| aGVHD III-IVe | 1 | 0.86 (0.61–1.23) | 0.74 (0.40–1.37) | 0.53 |
| cGVHDf | 1 | 0.88 (0.69–1.12) | 1.20 (0.85–1.70) | 0.21 |
| Haplotype 2: G TA | no GTA | one GTA | GTA/GTA | Overall p-value |
|---|---|---|---|---|
|
| ||||
| Ref | HR (95% CI) | HR (95% CI) | ||
| Overall survivala | 1 | 1.04 (0.82–1.31) | 1.26 (0.84–1.87) | 0.53 |
| NRMb | 1 | 1.01 (0.78–1.32) | 1.17 (0.71–1.91) | 0.83 |
| Relapsec | 1 | 1.53 (0.98–2.39) | 2.51 (1.27–4.97) | 0.02 |
| aGVHD II-IVd | 1 | 0.98 (0.78–1.24) | 0.72 (0.46–1.15) | 0.39 |
| aGVHD III-IVe | 1 | 0.94 (0.67–1.34) | 0.48 (0.21–1.11) | 0.23 |
| cGVHDf | 1 | 1.09 (0.87–1.38) | 0.88 (0.57–1.36) | 0.57 |
| Haplotype 3: TTA | no TTA | one TTA | TTA/TTA | Overall p-value |
|---|---|---|---|---|
|
| ||||
| Ref | HR (95% CI) | HR (95% CI) | ||
| Overall survivala | 1 | 0.95 (0.73–1.24) | NA | 0.69 |
| NRMb | 1 | 0.88 (0.65–1.19) | NA | 0.42 |
| Relapsec | 1 | 1.06 (0.62–1.79) | NA | 0.85 |
| aGVHD II-IVd | 1 | 0.79 (0.59–1.03) | NA | 0.08 |
| aGVHD III-IVe | 1 | 0.98 (0.66–1.46) | NA | 0.92 |
| cGVHDf | 1 | 0.99 (0.77–1.29) | NA | 0.99 |
| Haplotype 4: GCA | no GCA | one GCA | GCA/GCA | Overall p-value |
|---|---|---|---|---|
|
| ||||
| Ref | HR (95% CI) | HR (95% CI) | ||
| Overall survivala | 1 | 1.04 (0.83–1.31) | 1.19 (0.80–1.77) | 0.64 |
| NRMb | 1 | 1.10 (0.85–1.44) | 1.28 (0.82–2.01) | 0.48 |
| Relapsec | 1 | 0.67 (0.42–1.05) | 0.61 (0.26–1.44) | 0.26 |
| aGVHD II-IVd | 1 | 0.96 (0.76–1.21) | 1.02 (0.67–1.55) | 0.94 |
| aGVHD III-IVe | 1 | 1.10 (0.77–1.57) | 1.75 (0.98–3.12) | 0.17 |
| cGVHDf | 1 | 0.96 (0.76–1.20) | 0.84 (0.54–1.31) | 0.74 |
Adjusted for patient age, year of transplant, disease and stratified by disease stage.
Adjusted for use of ATG, year of transplant and stratified by patient age.
Adjusted for disease, Karnofsky score, time from diagnosis to transplant and number of donor pregnancies.
Adjusted for disease stage, disease, graft type and GVHD prophylaxis.
Adjusted for disease, graft type, year of transplant and number of donor pregnancies.
Adjusted for disease, graft type, ATG GVHD prophylaxis, donor sex and number of donor pregnancies.
Reconstitution of the T cell population after HCT is dependent on a combination of thymus-dependent de novo T cell production and extrathymic expansion of donor-derived T cells. Since IL-7 can be involved in both processes (Carrette and Surh, 2012; Hong et al., 2012), we hypothesized that genetic variations in the IL-7Rα gene could influence T cell reconstitution and the risk of GvHD and NRM. Indeed, previous studies of single SNPs suggested that genetic variations in the donor is associated with the outcome after HCT. Specifically, rs1494555GG and rs1494558TT donor genotypes were associated with acute and chronic GvHD in a univariate analysis (Shamim et al., 2006; Shamim et al., 2013). The T allele of rs6897932, which was shown to be associated with the risk of developing multiple sclerosis (MS) (Gregory et al., 2007; Hafler et al., 2007; Lundmark et al., 2007) and inhalation allergy (Shamim et al., 2007), was suggestive of an association with increased frequency of relapse by univariate analysis and multivariate analysis (Shamim et al., 2013).
In this study, validation of these results using haplotypes instead of single SNPs was performed in the same cohort of donors. SNPs in the IL-7Rα gene combine into 4 common haplotypes, which can be determined using 3 tagging SNPs in the promoter region. This approach has been used successfully in the past in the context of MS (Booth et al., 2005; Broux et al., 2010) and human immunodeficiency virus (HIV) (Rajasuriar et al., 2010; Rajasuriar et al., 2012), validating the use of this technique for identification of multiple SNPs in one assay, resulting in an increase of information per assay. We observed a trend for increased risk of relapse when donors carried the Hap2/Hap2 genotype (containing the rs6897932T SNP), in line with the previous findings in a univariate analysis in the single SNP study (Shamim et al., 2013). Due to the limited size of this group, the statistic outcome was not considered to be conclusive. The SNPs rs1494555G and rs1494558T are in strong linkage disequilibrium with each other (Shamim et al., 2013) and are located in Hap1. Although these SNPs have previously been associated with aGvHD, we were not able to identify any statistically significant association for Hap1, indicating that other SNPs present in this haplotype do not add to or even decrease the risk previously identified at the single SNP level.
The apparent discrepancy between this study and the previous study of single SNPs in the same cohort may also be related to a lower power in the present study, due to “dilution” of SNPs into haplotypes. For example, when analyzing the rs6897932 SNP, only three combinations are found (TT, CT, CC). However, when analyzing the four haplotypes, 10 combinations are possible, of which 4 contain Hap2 (the haplotype carrying rs6897932T). This dramatically reduces the number of individuals and events in each group, decreasing the power of the study. Here, we genotyped 591 donor/recipient pairs, which might not be sufficient to detect any significant effects using this approach. Future studies using a larger population of donor/recipient pairs will provide a decisive validation of these results and a conclusive statement on whether IL-7Rα genotyping can contribute to better clinical outcome after HCT.
Acknowledgments
This project was funded by Hasselt University, Fonds voor Wetenschappelijk Onderzoek, Belgian Charcot Foundation, University Hospital Rigshospitalet Copenhagen, the Dagmar Marshall Foundation and the Danish Cancer Society.
The CIBMTR is supported by Public Health Service Grant/Cooperative Agreement U24-CA076518 from the National Cancer Institute (NCI), the National Heart, Lung and Blood Institute (NHLBI) and the National Institute of Allergy and Infectious Diseases (NIAID); a Grant/Cooperative Agreement 5U10HL069294 from NHLBI and NCI; a contract HHSH250201200016C with Health Resources and Services Administration (HRSA/DHHS); two Grants N00014-14-1-0028 and N00014-13-1-0039 from the Office of Naval Research; and grants from Actinium Pharmaceuticals; Allos Therapeutics, Inc.; Amgen, Inc.; Anonymous donation to the Medical College of Wisconsin; Ariad; Be the Match Foundation; Blue Cross and Blue Shield Association; Celgene Corporation; Chimerix, Inc.; Fred Hutchinson Cancer Research Center; Fresenius-Biotech North America, Inc.; Gamida Cell Teva Joint Venture Ltd.; Genentech, Inc.; Gentium SpA; Genzyme Corporation; GlaxoSmithKline; Health Research, Inc. Roswell Park Cancer Institute; HistoGenetics, Inc.; Incyte Corporation; Jeff Gordon Children’s Foundation; Kiadis Pharma; The Leukemia & Lymphoma Society; Medac GmbH; The Medical College of Wisconsin; Merck & Co, Inc.; Millennium: The Takeda Oncology Co.; Milliman USA, Inc.; Miltenyi Biotec, Inc.; National Marrow Donor Program; Onyx Pharmaceuticals; Optum Healthcare Solutions, Inc.; Osiris Therapeutics, Inc.; Otsuka America Pharmaceutical, Inc.; Perkin Elmer, Inc.; Remedy Informatics; Sanofi US; Seattle Genetics; Sigma-Tau Pharmaceuticals; Soligenix, Inc.; St. Baldrick’s Foundation; StemCyte, A Global Cord Blood Therapeutics Co.; Stemsoft Software, Inc.; Swedish Orphan Biovitrum; Tarix Pharmaceuticals; TerumoBCT; *Teva Neuroscience, Inc.; THERAKOS, Inc.; University of Minnesota; University of Utah; and Wellpoint, Inc. The views expressed in this article do not reflect the official policy or position of the National Institute of Health, the Department of the Navy, the Department of Defense, Health Resources and Services Administration (HRSA) or any other agency of the U.S. Government.
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