Abstract
Glucocorticoids are central to effective therapy of acute graft-versus-host disease (GVHD). However, only about half of the patients respond to steroids in initial therapy. Based on postulated mechanisms for anti-inflammatory effectiveness, we explored genetic-variations in glucocorticoid receptor, co-chaperone proteins, membrane transporters, inflammatory mediators, and variants in the T-cell receptor complex in hematopoietic cell transplant recipients with acute GVHD requiring treatment with steroids and their donors towards response at day 28 after initiation of therapy. 300 recipient and donor samples were analyzed. Twenty-three SNPs in 17 genes affecting glucocorticoid pathways were included in the analysis. In multiple regression analysis, donor SNP rs3192177 in the ZAP70 gene (O.R. 2.8, 95% CI: 1.3–6.0, p=0.008), and donor SNP rs34471628 in the DUSPI gene (O.R. 0.3, 95% CI: 0.1–1.0, p=0.048) were significantly associated with complete or partial response. However, after adjustment for multiple testing, these SNPs did not remain statistically significant. Our results, on this small, exploratory, hypothesis generating analysis suggest that common genetic variation in glucocorticoid pathways may help identify subjects with differential response to glucocorticoids. This needs further assessment in larger datasets, and if validated, could help identify subjects for alternative treatments and design targeted treatments to overcome steroid resistance.
Keywords: Allogeneic, HCT, graft-versus-host disease, pharmacogenetics, SNPs
Introduction
Glucocorticoids are central to effective therapy of acute graft-versus-host disease (GVHD). However, only about half of the patients respond and only one third have a durable response1. Several studies have evaluated clinical and biological parameters to predict response at day 28, however none has explored glucocorticoid pathways directly to identify steroid resistance in acute GVHD2–5. Based on postulated mechanisms for anti-inflammatory effectiveness6,7, we explored genetic variations in glucocorticoid receptor (GR), co-chaperone proteins, membrane transporters, inflammatory mediators, and variants in the T-cell receptor complex in hematopoietic cell transplant (HCT) recipients with acute GVHD requiring treatment with steroids and their donors towards response at day 28 after initiation of therapy.
Patients and Methods
175 HCT recipients with acute graft-versus-host disease8 requiring treatment with systemic steroids, who underwent a HCT between 2003–2008 at the University of MN or enrolled in the BMT CTN 0302 trial9, with either recipient or donor DNA available were included in this study (87 from University of MN and 88 from CTN 0302). 300 samples (149 recipients and 151 donors) were available for this preliminary study. Fifty- three SNPs were genotyped. SNPs with very low variation in our cohort (< 4% in both donor and recipient sample) were excluded from analysis. Twenty-three SNPs in 17 genes affecting glucocorticoid pathways were included in the analysis. Our endpoints were overall response (complete or partial response: CR/PR) and CR alone. Response was graded as described previously by MacMillan et al for both the University of MN and BMT CTN cohorts.2 This study was approved by the Institutional Review Board (IRB) at the University of Minnesota.
Results
Recipients ranged in age from < 1 to 68 years, 63% underwent a HCT using a myeloablative conditioning, 29% underwent a HCT from an HLA-identical sibling donor, 26% from matched URD and 39% from an umbilical cord blood (Table 1). Acute GVHD grade at onset included grade I disease in 27 recipients (15%), grade II in 109 recipients (62%), grade III in 36 (21%) and grade IV in 3 (2%). All recipients received initial therapy with systemic steroids. Patients enrolled in BMT CTN 0302 were in addition treated with etanercept (n=21, 24%), mycophenolate mofetil (MMF) (N=21, 24%), Ontak (n=21, 24%) or Pentostatin (n=25, 28%). Initial multivariate logistic regression analysis was performed to determine clinical risk factors associated with CR or PR. Clinical variables included in the analysis were recipient age, conditioning regimen, donor type, acute GVHD grade and year of HCT. Only year of HCT was significantly associated with CR or CR+ PR (p=0.01). Each recipient and donor SNP was subsequently evaluated individually in logistic regression analysis after controlling for year of HCT.
Table 1.
Demographic Characteristics
| N | N with CR (%) | N with CR/PR (%) | |
|---|---|---|---|
| N | 175 | 99 (57%) | 136 (78%) |
| Conditioning regimen | |||
| Myeloablative | 111 | 60 (54%) | 82 (74%) |
| Reduced intensity conditioning | 64 | 39 (61%) | 54 (84%) |
| Gender | |||
| Male | 114 | 61 (54%) | 86 (75%) |
| Female | 61 | 38 (62%) | 50 (82%) |
| Recipient age (years) | |||
| Age 0–20 | 32 | 22 (69%) | 27 (84%) |
| Age 21–40 | 39 | 17 (44%) | 30 (77%) |
| Age 41–68 | 104 | 60 (58%) | 79 (76%) |
| Source | |||
| Clinical Trials Network (CTN 0302) | 88 | 42 (48%) | 63 (72%) |
| University of Minnesota | 87 | 57 (65%) | 73 (84%) |
| Donor type | |||
| Matched sibling | 51 | 31 (61%) | 42 (82%) |
| Other related | 7 | 3 (43%) | 5 (71%) |
| UCB | 68 | 42 (62%) | 57 (84%) |
| Matched URD | 46 | 23 (50%) | 31 (67%) |
| Mismatched URD | 3 | 0 (0%) | 1 (33%) |
| Diagnosis | |||
| ALL | 47 | 30 (64%) | 37 (79%) |
| AML | 33 | 12 (36%) | 21 (64%) |
| CML | 12 | 8 (67%) | 9 (75%) |
| Other leukemia | 12 | 8 (67%) | 11 (92%) |
| MDS | 20 | 11 (55%) | 13 (65%) |
| NHL | 30 | 17 (57%) | 25 (83%) |
| Multiple Myeloma | 4 | 2 (50%) | 4 (100%) |
| Other | 17 | 11 (65%) | 16 (94%) |
| AGVHD grade at onset | |||
| 1 | 27 | 17 (63%) | 21 (78%) |
| 2 | 109 | 63 (58%) | 89 (82%) |
| 3 | 36 | 17 (47%) | 24 (67%) |
| 4 | 3 | 2 (67%) | 2 (67%) |
| AGVHD treatment (CTN 0302) | |||
| Etanercept | 21 | 5 (24%) | 13 (62%) |
| MMF | 21 | 10 (48%) | 16 (76%) |
| Ontak | 21 | 17 (81%) | 18 (86%) |
| Pentostatin | 25 | 10 (40%) | 16 (64%) |
| Transplant year | |||
| 2003 | 4 | 3 (75%) | 4 (100%) |
| 2004 | 29 | 22 (76%) | 27 (93%) |
| 2005 | 33 | 21 (64%) | 25 (76%) |
| 2006 | 50 | 28 (56%) | 37 (74%) |
| 2007 | 53 | 21 (40%) | 37 (70%) |
| 2008 | 6 | 4 (67%) | 6 (100%) |
Abbreviations: CR: complete response; CR/PR: complete/partial response; UCB: umbilical cord blood; URD: unrelated donor; AML: acute myeloid leukemia; ALL: acute lymphoblastic leukemia; CML: chronic myeloid leukemia; MDS: myelodysplastic syndrome; NHL: non hodgkin’s lymphoma; AGVHD: acute graft-versus-host disease
Results of analysis are presented in Table 2. The reference category was the genotype consisting of two copies of the more common allele. The odds ratio represents the change in odds for each copy of the less common allele. The Holm-Sidak10 method was used to adjust p-values for multiple testing This method attempts to control the family wise error rate at no more than 5%. All SNPs were checked for Hardy-Weinberg equilibrium. SNPs rs864058 and rs179008 (both in TLR7 gene) were identified not to be in Hardy-Weinberg equilibrium (p<0.02).
Table 2.
Multivariate analysis of SNPs as predictors of response (CR; CR or PR)
| Gene | Outcome = CR | Outcome = CR or PR | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SNP (r= recipient d=donor) | Minor allele frequency | Odds Ratio | Lower 95% CL | Upper 95% CL | p-value | Adjusted p-value | Odds Ratio | Lower 95% CL | Upper 95% CL | p-value | Adjusted p-value | |
| ABCB1 (MDR1) | r_rs1045642 | 0.47 | 0.9 | 0.6 | 1.5 | 0.814 | 1.000 | 1.1 | 0.7 | 1.9 | 0.665 | 1.000 |
| d_rs1045642 | 0.46 | 1.1 | 0.7 | 1.7 | 0.808 | 1.000 | 1.0 | 0.6 | 1.7 | 0.880 | 1.000 | |
| ANXA-1 (annexin-1) | r_rs1050305 | 0.08 | 0.5 | 0.2 | 1.3 | 0.136 | 1.000 | 0.6 | 0.2 | 1.7 | 0.370 | 1.000 |
| d_rs1050305 | 0.07 | 0.6 | 0.2 | 1.7 | 0.353 | 1.000 | 0.6 | 0.2 | 1.7 | 0.331 | 1.000 | |
| NFKBIA (IkBa) | r_rs1050851 | 0.24 | 1.3 | 0.6 | 2.6 | 0.505 | 1.000 | 1.2 | 0.5 | 2.8 | 0.608 | 1.000 |
| d_rs1050851 | 0.24 | 0.6 | 0.3 | 1.3 | 0.206 | 1.000 | 0.8 | 0.4 | 1.8 | 0.654 | 1.000 | |
| HSP90AA1 (Hsp90) | r_rs10873531 | 0.18 | 0.8 | 0.4 | 1.6 | 0.466 | 1.000 | 1.0 | 0.4 | 2.2 | 0.921 | 1.000 |
| d_rs10873531 | 0.10 | 1.6 | 0.6 | 3.8 | 0.321 | 1.000 | 1.9 | 0.6 | 5.9 | 0.293 | 1.000 | |
| DNAJA1 (Hsp40) | r_rs20583 | 0.48 | 0.9 | 0.6 | 1.6 | 0.836 | 1.000 | 1.4 | 0.7 | 2.6 | 0.335 | 1.000 |
| d_rs20583 | 0.41 | 1.2 | 0.7 | 2.0 | 0.519 | 1.000 | 1.1 | 0.6 | 1.9 | 0.868 | 1.000 | |
| STIP1 (HOP) | r_rs2070232 | 0.07 | 1.7 | 0.6 | 4.6 | 0.312 | 1.000 | 1.2 | 0.4 | 4.1 | 0.720 | 1.000 |
| d_rs2070232 | 0.07 | 1.3 | 0.5 | 3.6 | 0.645 | 1.000 | 1.5 | 0.4 | 5.8 | 0.519 | 1.000 | |
| ABCB1 (MDR1) | r_rs2229109 | 0.05 | 1.7 | 0.5 | 6.0 | 0.440 | 1.000 | 1.5 | 0.3 | 7.3 | 0.624 | 1.000 |
| d_rs2229109 | 0.02 | 0.5 | 0.1 | 2.4 | 0.364 | 1.000 | 1.6 | 0.2 | 15 | 0.662 | 1.000 | |
| PPID | r_rs2230221 | 0.03 | 0.7 | 0.2 | 3.0 | 0.612 | 1.000 | 0.4 | 0.1 | 1.9 | 0.247 | 1.000 |
| d_rs2230221 | 0.07 | 1.1 | 0.4 | 2.9 | 0.876 | 1.000 | 0.7 | 0.2 | 2.1 | 0.556 | 1.000 | |
| DUSP1 (MKP-1) | r_rs2431663 | 0.08 | 0.7 | 0.3 | 1.7 | 0.379 | 1.000 | 0.4 | 0.2 | 1.2 | 0.094 | 1.000 |
| d_rs2431663 | 0.04 | 0.4 | 0.1 | 1.6 | 0.221 | 1.000 | 0.3 | 0.1 | 1.2 | 0.089 | 1.000 | |
| ZAP70 | r_rs3192177 | 0.33 | 1.2 | 0.7 | 1.9 | 0.576 | 1.000 | 1.4 | 0.8 | 2.6 | 0.287 | 1.000 |
| d_rs3192177 | 0.28 | 1.6 | 0.9 | 2.8 | 0.108 | 1.000 | 2.8 | 1.3 | 6.0 | 0.008 | 0.405 | |
| CD247 (Zeta) | r_rs33937946 | 0.05 | 2.1 | 0.6 | 7.1 | 0.249 | 1.000 | 1.7 | 0.4 | 8.1 | 0.514 | 1.000 |
| d_rs33937946 | 0.04 | 0.9 | 0.3 | 3.1 | 0.875 | 1.000 | 0.5 | 0.1 | 1.8 | 0.286 | 1.000 | |
| DUSP1 (MKP-1) | r_rs34471628 | 0.05 | 0.7 | 0.2 | 2.2 | 0.531 | 1.000 | 0.5 | 0.1 | 1.6 | 0.241 | 1.000 |
| d_rs34471628 | 0.03 | 0.5 | 0.1 | 1.9 | 0.301 | 1.000 | 0.3 | 0.1 | 1.0 | 0.048 | 1.000 | |
| FKBP5 (FKBP51) | r_rs34866878 | 0.02 | 1.1 | 0.2 | 5.2 | 0.951 | 1.000 | 0.8 | 0.1 | 4.2 | 0.744 | 1.000 |
| d_rs34866878 | 0.04 | 1.4 | 0.4 | 5.1 | 0.630 | 1.000 | 0.5 | 0.1 | 2.0 | 0.335 | 1.000 | |
| TLR 9 | r_rs352140 | 0.48 | 1.0 | 0.6 | 1.7 | 0.935 | 1.000 | 0.8 | 0.5 | 1.5 | 0.534 | 1.000 |
| d_rs352140 | 0.49 | 1.4 | 0.9 | 2.3 | 0.156 | 1.000 | 1.3 | 0.7 | 2.2 | 0.410 | 1.000 | |
| CD3G (Gamma) | r_rs3753058 | 0.17 | 0.8 | 0.4 | 1.7 | 0.625 | 1.000 | 1.0 | 0.4 | 2.3 | 0.959 | 1.000 |
| d_rs3753058 | 0.17 | 0.5 | 0.2 | 1.0 | 0.060 | 1.000 | 0.5 | 0.2 | 1.1 | 0.079 | 1.000 | |
| CD3E (Epsilon) | r_rs4606515 | 0.33 | 1.3 | 0.8 | 2.2 | 0.325 | 1.000 | 1.1 | 0.6 | 1.9 | 0.810 | 1.000 |
| d_rs4606515 | 0.34 | 1.4 | 0.8 | 2.5 | 0.206 | 1.000 | 1.0 | 0.5 | 1.9 | 0.939 | 1.000 | |
| HSD11B2 (11B-HSD T2) | r_rs5479 | 0.05 | 1.0 | 0.3 | 3.2 | 0.955 | 1.000 | 1.1 | 0.3 | 4.1 | 0.920 | 1.000 |
| d_rs5479 | 0.06 | 1.3 | 0.5 | 3.6 | 0.638 | 1.000 | 1.7 | 0.5 | 6.5 | 0.413 | 1.000 | |
| NR3C1 (GR) | r_rs6196 | 0.16 | 0.9 | 0.4 | 1.9 | 0.806 | 1.000 | 0.5 | 0.2 | 1.2 | 0.126 | 1.000 |
| d_rs6196 | 0.13 | 0.7 | 0.3 | 1.6 | 0.433 | 1.000 | 0.7 | 0.3 | 1.6 | 0.357 | 1.000 | |
| HSP90AA1 (Hsp90) | r_rs8005905 | 0.14 | 0.8 | 0.4 | 1.6 | 0.490 | 1.000 | 1.1 | 0.4 | 2.7 | 0.897 | 1.000 |
| d_rs8005905 | 0.07 | 1.9 | 0.7 | 5.5 | 0.226 | 1.000 | 1.3 | 0.4 | 4.2 | 0.705 | 1.000 | |
| ABCB1 (MDR1) | r_rs9282564 | 0.09 | 0.9 | 0.3 | 2.1 | 0.733 | 1.000 | 1.5 | 0.5 | 4.9 | 0.467 | 1.000 |
| d_rs9282564 | 0.11 | 1.5 | 0.6 | 3.6 | 0.362 | 1.000 | 2.3 | 0.7 | 7.2 | 0.167 | 1.000 | |
| PPID (peptidylpropyl isomerase D | r_rs9410 | 0.26 | 0.9 | 0.5 | 1.5 | 0.603 | 1.000 | 0.9 | 0.5 | 1.8 | 0.787 | 1.000 |
| d_rs9410 | 0.31 | 1.0 | 0.6 | 1.7 | 0.899 | 1.000 | 1.3 | 0.7 | 2.4 | 0.379 | 1.000 | |
Abbreviation: CR: complete response, CR/PR: complete or partial response
Outcomes evaluated include complete response (CR) and complete or partial response (CR/PR). After excluding SNPs that were not in Hardy-Weinberg equilibrium, donor SNP rs3192177 in the ZAP70 gene, and donor SNP rs34471628 in the DUSPI gene were significantly associated with CR/PR. However, after adjustment for multiple testing, none of the SNPs remained statistically significant.
Discussion
T-cells have been shown to be critical cellular targets of GR signaling as T cell-specific GR knockout mice show significant mortality following immune activation.11 It has been shown that T cells defective in Zap70 signaling show loss of retinoid receptor dependent transcription.12 It has also been demonstrated that Zap70 signaling is also essential in dexamethasone inducible GR-mediated transactivation in T lymphocytes.13 Zap70 and its upstream kinase Lck have been identified to be essential enzymes in maintaining normal levels of ANXA1, a protein known to be induced and released by the glucocorticoid action. Zap70 and Lck were also found to regulate ANXA1 promoter function in part through the modulation of GR-dependent transcription13.
Another mechanism of action by which glucocorticoids inhibit inflammation is through induction of the dual specificity phosphatase-1 (DUSP-1) gene, which reduces the activation of mitogen activated protein kinases (MAPKs)14. MAPKs can enhance the transcriptional activity of NF-κB and AP-1, and also cause mRNA stabilization and translational activation of inflammatory genes14.
Our results, on this small, exploratory, hypothesis generating analysis suggest that common genetic variation in glucocorticoid pathways may help identify subjects who may be less likely to respond to gluco-corticoids. This needs further assessment in larger datasets, and if validated, could help identify subjects for alternative treatments and also help design targeted treatments to overcome steroid resistance.
Acknowledgments
Support for this study was provided by grant #U10HL069294 to the Blood and Marrow Transplant Clinical Trials Network from the National Heart, Lung, and Blood Institute and the National Cancer Institute, along with contributions by Eisai Inc., Hospira Inc., Roche Laboratories Inc., and Immunex Corporation, a wholly owned subsidiary of Amgen Inc. The content is solely the responsibility of the authors and does not necessarily represent the official views of the above mentioned parties.
Additional support was provided to the Blood and Marrow Transplant Clinical Trials Network by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases for the ancillary studies, Analysis of Serum Biomarkers Related to aGVHD Treatment Responsiveness’ and ‘Pharmacogenetics of Steroid Responsiveness in aGVHD’. This manuscript was prepared using BMT CTN 0302 Research Materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center and does not necessarily reflect the opinions or views of the BMT CTN 0302 or the NHLBI.
We also acknowledge all centers who enrolled in the BMT CTN 0302 trial.
Footnotes
Conflict-of-interest disclosure: The authors declare no competing financial interests.
Author contributions
M.A. and B.T. were involved in the concept/design, data analysis/interpretation, drafting article and critical revision of article. D.W. reviewed the data analysis, and contributed to critical revision of the article. R.S. performed the data analysis and contributed to critical revision of the article. All authors approved the final article.
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