Key Points

  • A risk model using donor and recipient cytokine gene polymorphisms and clinical variables significantly improves GVHD risk stratification.

  • The model is useful in identifying patients with low-risk of developing severe GVHD, but results must be confirmed in prospective studies.

Abstract

Despite considerable advances in our understanding of the pathophysiology of graft-versus-host disease (GVHD), its prediction remains unresolved and depends mainly on clinical data. The aim of this study is to build a predictive model based on clinical variables and cytokine gene polymorphism for predicting acute GVHD (aGVHD) and chronic GVHD (cGVHD) from the analysis of a large cohort of HLA-identical sibling donor allogeneic stem cell transplant (allo-SCT) patients. A total of 25 SNPs in 12 cytokine genes were evaluated in 509 patients. Data were analyzed using a linear regression model and the least absolute shrinkage and selection operator (LASSO). The statistical model was constructed by randomly selecting 85% of cases (training set), and the predictive ability was confirmed based on the remaining 15% of cases (test set). Models including clinical and genetic variables (CG-M) predicted severe aGVHD significantly better than models including only clinical variables (C-M) or only genetic variables (G-M). For grades 3-4 aGVHD, the correct classification rates (CCR1) were: 100% for CG-M, 88% for G-M, and 50% for C-M. On the other hand, CG-M and G-M predicted extensive cGVHD better than C-M (CCR1: 80% vs. 66.7%, respectively). A risk score was calculated based on LASSO multivariate analyses. It was able to correctly stratify patients who developed grades 3-4 aGVHD (P < .001) and extensive cGVHD (P < .001). The novel predictive models proposed here improve the prediction of severe GVHD after allo-SCT. This approach could facilitate personalized risk-adapted clinical management of patients undergoing allo-SCT.

Introduction

Allogeneic hematopoietic stem-cell transplantation (allo-SCT) is a curative therapeutic approach for patients with hematologic malignancies. Patients undergoing allo-SCT receive a donor graft containing hematopoietic stem cells, as well as various other cell types, including alloreactive T cells. T cells promote hematopoietic engraftment, T-cell immunity reconstitution, and mediate graft-versus-leukemia effect, which may prevent tumor relapse. However, donor T cells may also cause graft-versus-host disease (GVHD), which is the main complication after allo-SCT and the most important cause of nonrelapse morbidity and nonrelapse mortality (NRM).1 

There are 2 forms of GVHD, acute GVHD (aGVHD) and chronic GVHD (cGVHD). aGVHD is a complex process that takes place in 3 phases.2  In the first phase, conditioning regimen damages host tissues and raises levels of proinflammatory cytokines such as interleukin-1 (IL-1), IL-6, tumor necrosis factor α (TNFα), and interferon-γ (IFN-γ), thus activating host antigen-presenting cells, which stimulate donor T cells. In the second phase, this interaction induces proliferation and differentiation of donor T cells, which in turn leads to rapid intracellular biochemical cascades that induce transcription of genes for many proteins (including cytokines TNFα, IFN-γ, and IL-2) and promote cellular activity. The third effector phase is a complex cascade of both cellular mediators and soluble inflammatory mediators such as TNFα, IFN-γ, IL-1, and nitric oxide, resulting in tissue injury. Although the pathophysiology of cGVHD is less known, significant advances in our understanding have been made in recent years, and it is now evident that the clinical manifestations result from a complex immune disease involving both donor B cells and T cells.3  The long-standing hypothesis is that cGVHD is similar to an autoimmune disorder.4  It is well established that the most important risk factor for the development of GVHD is the degree of HLA matching between the recipient and the donor,5,6  although a significant proportion of patients undergoing transplantation with HLA-identical grafts develop aGVHD1  and/or cGHVD.7  Consequently, other non-HLA factors contribute to the development of this complication. Major clinical factors associated with GVHD include patient age, sex of donor/recipient,8  stem-cell source,9  GVHD prophylaxis, underlying disease, conditioning regimen,10  and, for cGVHD, a history of aGVHD.

Genetic differences in non-HLA genes between recipients and donors are also important,2  and the role of polymorphisms in human minor histocompatibility antigens,11,12  innate immunity genes,13-15  genes involved in drug metabolism, and proinflammatory cytokines must be taken into account.16,17  During the past decade, single-nucleotide polymorphisms (SNPs) have been identified in genes involved in innate and adaptive immune responses, such as cytokines and their receptors, which have a role in the classic cytokine storm of GVHD.18-21  However, information regarding the diagnostic, prognostic, and predictive significance of these molecules in GVHD is limited. Although clinically useful biomarkers are available, no particular biomarker alone is generally satisfactory in terms of sensitivity or specificity for the diagnosis or prediction of a disease. Therefore, it is important to build biomarker panels and risk models for GVHD. In recent years, many groups have been working in this field. Kim et al22,23  built a risk model incorporating SNPs and clinical markers to stratify patients and more accurately predict the risk of GVHD in specific organs, Paczesny et al24  developed protein panels that provide meaningful information to confirm the diagnosis of GVHD in patients at the onset of clinical symptoms of GVHD and provide useful data for prognosis.25 

After genotyping a panel of polymorphisms in cytokine genes that had been previously associated with aGVHD or cGVHD,7,18,26  we applied a complex estimation method, the least absolute shrinkage and selection operator (LASSO) procedure,27  which is able to group optimal predictors from a large set of potential clinical and genetic predictor variables, improving their clinical utility.

Methods

Study design

This retrospective study included 509 patients with hematological malignancies from the Spanish Group for Hematopoietic Transplantation (GETH) who underwent conventional HLA-identical sibling-donor allo-SCT between 1997 and 2010 at 11 Spanish institutions (the mean number of patients from each center was 46.3 [range, 25-134]; supplemental Table 1). The median follow-up for living patients was 14.7 months (range, 2-105.4 months).

Only patients for whom all clinical and genetic data were available (a prerequisite of the LASSO procedure) were finally included in the analysis (n = 359; Table 1). Patients who died before day +100 without aGVHD (n = 96) or day +200 without cGVHD (n = 154) were excluded from the LASSO multivariate analyses, which therefore included 263 patients for aGVHD and 207 patients for cGVHD modeling.

Table 1.

Clinical characteristics of patients

CharacteristicAll cohort (n = 359)aGVHD cohort (n = 263)cGVHD cohort (n = 207)
Follow-up, mo    
 Median (range) 32 (4-105) 23 (1-104) 33 (4-104) 
Age, y    
 Median (range) 45 (0-68) 44 (0-68) 42 (0-68) 
Patient sex, n (%)    
 Male 225 (63) 166 (63) 131 (63) 
 Female 134 (37) 97 (37) 76 (37) 
Donor sex, n (%)    
 Male 201 (56) 147 (56) 115 (55) 
 Female 158 (44) 116 (44) 92 (45) 
Donor/recipient sex, n (%)    
 Female donor/male recipient 91 (25) 69 (26) 53 (26) 
Disease, n (%)    
 AML 116 (32) 73 (28) 52 (25) 
 ALL 49 (13.5) 37 (14) 30 (14.5) 
 NHL, HD 54 (15) 39 (15) 29 (14) 
 Myelofibrosis, MDS 34 (9.5) 25 (9.5) 19 (9) 
 MM 33 (9) 25 (9.5) 18 (9) 
 Other (CML, AA, etc) 73 (20) 25 (9.5) 59 (28.5) 
Conditioning regimen, n (%)    
 Myeloablative 253 (70) 170 (65) 144 (70) 
 Reduced intensity 106 (30) 93 (35) 63 (30) 
 With TBI 94 (26) 71 (27) 59 (28.5) 
GVHD prophylaxis, n (%)    
 CsA + MTX 290 (81) 220 (83.5) 165 (79.7) 
 CsA ± steroids 8 (2) 4 (1.5) 3 (1.3) 
 Others 61 (17) 39 (15) 39 (18) 
Stem-cell source, n (%)    
 Peripheral blood 250 (69.6) 178 (68) 133 (64) 
 Bone marrow 109 (30.4) 85 (32) 74 (36) 
aGVHD grade, n (%)    
 2-4 115 (32) 74 (28) 48 (23) 
 3-4 50 (14) 29 (11) 21 (10) 
cGVHD, n (%)    
 Global 161 (45) 142 (54) 109 (53) 
 Extensive 100 (28) 84 (32) 63 (30) 
Mortality, n (%)    
 Incidence 86 (24) 27 31 
Relapse, n (%)    
 Incidence 105 (30) 74 (28) 53 (26) 
Death, n (%)    
 Incidence 154 (43) 96 (37) 54 (26) 
CharacteristicAll cohort (n = 359)aGVHD cohort (n = 263)cGVHD cohort (n = 207)
Follow-up, mo    
 Median (range) 32 (4-105) 23 (1-104) 33 (4-104) 
Age, y    
 Median (range) 45 (0-68) 44 (0-68) 42 (0-68) 
Patient sex, n (%)    
 Male 225 (63) 166 (63) 131 (63) 
 Female 134 (37) 97 (37) 76 (37) 
Donor sex, n (%)    
 Male 201 (56) 147 (56) 115 (55) 
 Female 158 (44) 116 (44) 92 (45) 
Donor/recipient sex, n (%)    
 Female donor/male recipient 91 (25) 69 (26) 53 (26) 
Disease, n (%)    
 AML 116 (32) 73 (28) 52 (25) 
 ALL 49 (13.5) 37 (14) 30 (14.5) 
 NHL, HD 54 (15) 39 (15) 29 (14) 
 Myelofibrosis, MDS 34 (9.5) 25 (9.5) 19 (9) 
 MM 33 (9) 25 (9.5) 18 (9) 
 Other (CML, AA, etc) 73 (20) 25 (9.5) 59 (28.5) 
Conditioning regimen, n (%)    
 Myeloablative 253 (70) 170 (65) 144 (70) 
 Reduced intensity 106 (30) 93 (35) 63 (30) 
 With TBI 94 (26) 71 (27) 59 (28.5) 
GVHD prophylaxis, n (%)    
 CsA + MTX 290 (81) 220 (83.5) 165 (79.7) 
 CsA ± steroids 8 (2) 4 (1.5) 3 (1.3) 
 Others 61 (17) 39 (15) 39 (18) 
Stem-cell source, n (%)    
 Peripheral blood 250 (69.6) 178 (68) 133 (64) 
 Bone marrow 109 (30.4) 85 (32) 74 (36) 
aGVHD grade, n (%)    
 2-4 115 (32) 74 (28) 48 (23) 
 3-4 50 (14) 29 (11) 21 (10) 
cGVHD, n (%)    
 Global 161 (45) 142 (54) 109 (53) 
 Extensive 100 (28) 84 (32) 63 (30) 
Mortality, n (%)    
 Incidence 86 (24) 27 31 
Relapse, n (%)    
 Incidence 105 (30) 74 (28) 53 (26) 
Death, n (%)    
 Incidence 154 (43) 96 (37) 54 (26) 

Table lists characteristics used to build the predictive models with LASSO (aGVHD, n = 263; cGVHD, n = 207; NRM, n = 359).

AA, aplastic anemia; ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CML, chronic myeloid leukemia; CsA, cyclosporin A; HD, Hodgkin disease; MDS, myelodysplastic syndrome; MM, multiple myeloma; MTX, methotrexate; NHL, non-Hodgkin lymphoma;

The study was approved by the ethics committee of Hospital General Universitario Gregorio Marañón, and all recipients and donors provided written informed consent according to the Declaration of Helsinki.

Polymorphism genotyping

DNA was obtained from EDTA anticoagulated peripheral blood samples collected at the pretransplantation evaluation included in the GETH DNA bank.

A total of 25 SNPs in 12 cytokine genes (supplemental Table 2) were selected for their potential role in the pathogenesis of GVHD or in any autoimmune disease in other studies.7,15,20  SNPs were genotyped using the MALDI-TOF MassARRAY iPLEX Gold platform (Sequenom, San Diego, CA) at CeGen (National Genotyping Centre, Santiago de Compostela, Spain).

Clinical and genetic variables

Three predictive models were constructed for each of the outcomes considered (grade 2-4 aGVHD, grade 3-4 aGVHD, cGVHD, extensive cGVHD, and NRM): 1 with clinical variables alone (C-M), 1 with genetic variables alone (G-M), and 1 with both clinical and genetic variables (CG-M).

Clinical variables included were donor and recipient sex, recipient age, female donor/male recipient, stem-cell source, conditioning regimen, total body irradiation (TBI)–containing regimen, and disease. Previous grade 2 to 4 aGVHD was included in the analysis of cGVHD. Genetic variables (supplemental Table 1) were assessed for donors and recipients, and 4 different models of transmission were considered (recessive, dominant, codominant, and additive). Therefore, 8 genetic variables were built for each SNP.

GVHD classification and clinical data collection were performed at the moment of GVHD diagnosis by the attending physician following the 1994 Consensus Conference on aGVHD grading28  and the National Institutes of Health criteria for diagnosis and staging of cGVHD.29 

Limitations

Extensive cGVHD is considered in the present study, which includes patients from 1997, although it is no longer used as an end point in clinical practice.

Statistical analysis

The descriptive analysis of the SNPs was performed using the SNPassoc R package (version 1.5-8). Univariate regression analysis was performed using logistic regression with the SNPassoc R package for SNPs and with IBM SPSS Statistics for Windows (version 21.0; IBM Corp, Armonk, NY). P < .05 was considered significant.

Multivariate regression analysis was performed using the LASSO procedure, which is being increasingly applied to overcome the challenges posed by high-dimensional data.

LASSO is an innovative estimation method for linear regression models developed in 1996 by Tibshirani,27  which is able to select a set of optimal predictors from a large set of potential predictor variables. This method constrains the sum of absolute values of the regression coefficients by means of a smoothing parameter (λ), shrinking the estimated coefficients toward 0. Because of this, it is considered a powerful method for variable selection, providing more interpretable models. The idea of LASSO is quite general and can be applied in other statistical models, such as the generalized linear models.

In this study, the response variable Y is a binary variable that denotes whether the patient is affected by GVHD/NRM or not (Y = 1 and Y = 0, respectively). In that sense, LASSO was considered a variable selection method under the estimation of a Logit regression model (which is a particular type of generalized linear model). In this model, the strength of the penalty term is controlled by a smoothing parameter (λ), so that the larger λ is, the more parsimonious the model is (if λ = 0, then all the predictors are considered in the final model). Because of this, it is important to find the optimal parameter λ, which provides the best predictive model to anticipate GVHD and NRM. To this end, λ was chosen by adhering to the principle of parsimony and maximizing the area under the receiver operating characteristic curve (AUC) and the correct classification rates (CCRs; global CCR); for patients who do not develop GVHD, CCR0; for patients who develop GVHD, CCR1) associated with the fitted models for a grid of 100 values of λ (supplemental Figure 1). Theoretically, the AUC takes values between 0 and 1, although the practical lower bound is 0.5. A perfect classifier has an AUC of 1. All clinical and genetic variables were included in the LASSO multivariate analysis independently of the P value.

The statistical model was fitted (goodness-of-fit assessment) by randomly selecting 85% of the data (training set: 85% of cases and 85% of controls, because sets were representative of our initial sample), and the predictive ability was computed with the remaining 15% (test set). To evaluate the performance and the predictive ability of each model, training and testing samples were randomly selected 100 times. The distribution of the CCR and the AUC over the 100 iterations was shown by means of box plots and a statistical summary of the results. LASSO multivariate regression analysis was shown as odds ratio, which is the exponential function of the β coefficient of LASSO (odds ratio = exp[β coefficient]).

Finally, for prediction purposes, the cutoff point between low and high risk was based on the proportion of patients who developed (Y = 1) and did not develop (Y = 0) GVHD or NRM.30  These proportions of Y = 1 were 0.28 for grades 2 to 4 aGVHD, 0.11 for grade 3 to 4 aGVHD, 0.53 for cGVHD, 0.30 for extensive cGVHD, and 0.24 for NRM.

Predictive models

On the basis of LASSO multivariate analyses, a risk score was calculated for grade 2 to 4 and grade 3 to 4 aGVHD, for cGVHD and extensive cGVHD, and for NRM. To build the predictive model, GVHD and NRM risk scores were weighted by the size of the effect on the β coefficient of each variable and a constant obtained by the LASSO procedure, within a risk score equation.31  Such risk scores were used to calculate the risk for each patient who was classified as low risk (when the risk score fell below the cutoff point) and high risk (risk score above the cutoff point).

Results

Descriptive analysis

Results of the descriptive analysis including genotype frequencies, Hardy-Weinberg equilibrium, and minor allele frequency of the 25 SNPs in donors and recipients are summarized in supplemental Table 3. Genotype frequencies were similar to those of the 1000 Genomes Project for the Spanish population and were in accordance with Hardy-Weinberg equilibrium, except for the frequencies of the IL-10 SNPs (rs1800871, rs1800872, and rs1800896), which were in linkage disequilibrium.

Univariate and multivariate analyses

The association between clinical and genetic variables in donors and in recipients with the development of aGVHD, cGVHD, and NRM was investigated using univariate analysis (summarized in Table 2; detailed in supplemental Table 4) and LASSO multivariate analysis (Tables 3-6).

Table 2.

Summary of the most important variables to predict GVHD and NRM in univariate analysis

VariablePolymorphismD/ROR*
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
Clinical        
 Recipient age >45 y      
 Recipient sex, male vs female       
 Female donor/male recipient       
 MDS       
 Conditioning, RIC vs MAC      
 Conditioning regimen, no TBI vs TBI       
 Stem-cell source, PB vs BM      
 aGVHD, grade 2-4      
Genetic        
 IL-1A rs1800587     
 IL-1B rs1143627     
      
 rs16944     
      
 rs1143634     
      
 IL-2 rs2069762     
 IL-6 rs1800795     
 IL-17A rs8193036     
 rs4711998    
      
 rs2275913     
 rs3819024     
      
 IL-23R rs6687620    
      
 rs11209026     
 TGFβ rs1800469     
 INF-γ rs2069705     
VariablePolymorphismD/ROR*
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
Clinical        
 Recipient age >45 y      
 Recipient sex, male vs female       
 Female donor/male recipient       
 MDS       
 Conditioning, RIC vs MAC      
 Conditioning regimen, no TBI vs TBI       
 Stem-cell source, PB vs BM      
 aGVHD, grade 2-4      
Genetic        
 IL-1A rs1800587     
 IL-1B rs1143627     
      
 rs16944     
      
 rs1143634     
      
 IL-2 rs2069762     
 IL-6 rs1800795     
 IL-17A rs8193036     
 rs4711998    
      
 rs2275913     
 rs3819024     
      
 IL-23R rs6687620    
      
 rs11209026     
 TGFβ rs1800469     
 INF-γ rs2069705     

All variables are statistically significant (P < .05).

BM, bone marrow; D, donor; H, higher risk; L, lower risk; MAC, myeloablative conditioning; OR, odds ratio; PB, peripheral blood; R, recipient; RIC, reduced-intensity conditioning.

*

OR = 1 (neutral); OR < 1 (L); OR > 1 (H).

Table 3.

LASSO multivariate analysis of the association between clinical variables and GVHD/NRM

Clinical variableOR*
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
Recipient sex, female vs male 0.872   0.684 0.805 
Recipient age > median 45 y   1.095 1.043 2.122 
Female donor/male recipient     1.178 
Diagnosis      
 AML  0.876   0.856 
 ALL      
 MDS   1.204  2.244 
 Lymphoma 1.181    1.232 
 Myelofibrosis 0.490     
 MM 0.657     
Conditioning, RIC vs MAC 0.957 0.760    
Conditioning regimen, no TBI vs TBI  0.838   1.311 
Stem-cell source, PB vs BM 1.151  1.470 2.360  
aGVHD 2-4   1.284 1.253  
Clinical variableOR*
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
Recipient sex, female vs male 0.872   0.684 0.805 
Recipient age > median 45 y   1.095 1.043 2.122 
Female donor/male recipient     1.178 
Diagnosis      
 AML  0.876   0.856 
 ALL      
 MDS   1.204  2.244 
 Lymphoma 1.181    1.232 
 Myelofibrosis 0.490     
 MM 0.657     
Conditioning, RIC vs MAC 0.957 0.760    
Conditioning regimen, no TBI vs TBI  0.838   1.311 
Stem-cell source, PB vs BM 1.151  1.470 2.360  
aGVHD 2-4   1.284 1.253  
*

OR values for the variables selected by the LASSO procedure (described in “Methods”). OR values were obtained with the exponential function of β coefficient, which compares the strength of the effect of each individual independent variable with the dependent variable. The higher the absolute value of β, the stronger the effect. β = 0, OR = 1 (neutral); β < 0, OR < 1 (L); β > 0, OR > 1 (H).

Table 4.

LASSO multivariate analysis of the association between genetic variables and GVHD/NRM

Genetic variablePolymorphismGenotypeD/RModel of transmissionOR*
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
IL-1A rs1800587 CT Cod   0.750  1.383 
IL-1B rs1143627 TT Dom 0.809     
   CT Cod  1.037    
   TT Dom   1.472 1.135  
   TT Cod   1.113   
   CC<CT<TT Add     0.95 
   CC<CT<TT Add   1.202 1.060  
  rs16944 GG Dom 0.893     
   CC Dom    1.020  
   AA<AG<GG Add  1.128    
  rs1143634 CC Dom  0.831 0.847   
   CC Cod  0.952 0.987   
   TT<CT<CC Add   0.993   
   TT Rec  2.763 1.071 1.604 0.681 
   TT<CT<CC Add   0.747   
IL-2 rs2069762 GG Rec  0.278 0.329 0.795 0.904 
   GT Cod  1.153 1.014   
   GG<GT<TT Add   0.736  1.032 
IL-6 rs1800795 GG Dom  1.471    
   GG Cod  0.587   0.758 
   CC Rec    0.768  
   CG Cod  1.025    
   GG Dom  0.877    
   CC Rec   0.922   
IL-7R rs1494555 CT Cod   1.316   
   CC Rec  1.703    
   CT Cod  0.813  1.596 0.843 
   TT Dom   1.071   
   CC<CT<TT Add   1.166   
IL-10 rs1800871 TT Rec    0.702  
   CT Cod  3.351 1.374   
   CT Cod 1.124     
   TT Rec  1.019    
   TT<CT<CC Add    1.104  
  rs1800872 AC Cod   1.004   
   AA Rec  1.005    
  rs1800896 AG Cod 1.246     
   AG Cod 1.139     
   GG<AG<AA Add   0.794 0.953  
   GG Rec   1.419   
IL-17A rs8193036 CC Rec  1.397 2.395 1.329  
   CT Cod   0.919 0.903  
   CC<CT<TT Add  0.833    
   CC Rec .,419 2.677    
   CC<CT<TT Add    0.858  
   CT Cod  0.705    
  rs3819024 AA Dom   1.149 1.254  
   GG Cod   1.002   
   AG Cod   0.747   
   GG Rec  0.869    
   GG Rec 1.126 1.386    
  rs4711998 AG Cod   1.092 1.071  
   GG Cod    0.990  
   AA Rec   0.746   
   GG Dom    0.712  
   AA<AG<GG Add  0.837    
   GG Dom   0.894 0.674  
   AG Cod 1.377  1.013   
   GG Cod   0.918   
  rs2275913 AG Cod   1.340   
   AA<AG<GG Add     0.835 
   AA Rec  0.077   1.617 
   AA Rec  1.424   1.446 
IL-17F rs763780 TT Dom   0.723   
IL-23R rs6687620 TT Rec  0.539  8.736  
   CC Dom   0.564   
   TT<CT<CC Add     1.620 
   TT Rec  0.691 3.402 1.013 1.661 
   TT<CT<CC Add  1.358   0.639 
   CT Cod    0.514  
  rs11209026 GG Dom   0.495 0.994 1.740 
   AA<AG<GG Add   0.776 0.943  
   AA Rec   0.181  1.740 
   AG Cod   1.110   
   AA<AG<GG Add  1.030    
INF-γ rs2430561 AT Cod   1.537 0.806  
   AA<AT<TT Add  0.958    
   AA Rec  1.778    
   AT Cod  0.771    
  rs2069705 CT Cod   0.641 0.755 0.966 
   TT Rec     1.114 
   CT Dom 0.880     
   TT<CT<CC Cod  1.296    
   CT Add    0.810  
TGFβ rs2241716 AA<AG<GG Add  0.990    
   GG Dom  0.979    
   GG Dom 0.359 0.154    
   AA<AG<GG Add  0.667    
  rs1800469 TT Rec  1.566 3.320   
   CC Dom 0.875     
   TT<CT<CC Add 0.994     
   CT Cod 1.008 0.956    
   TT Rec   0.280 0.722  
   TT<CT<CC Add    1.101  
TNFα rs1799964 CC Rec 0.968     
   TT Dom   1.237   
   CC Rec  1.556    
   CC<CT<TT Add  0.939    
  rs1800629 AG Cod  2.820    
   AA Rec   0.938   
  rs1800610 AA Rec   0.807 0.632  
   AA<AG<GG Add   1.128   
   TT Rec   8.854   
   AA<AG<GG Add     1.139 
   TT Rec  0.751    
Genetic variablePolymorphismGenotypeD/RModel of transmissionOR*
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
IL-1A rs1800587 CT Cod   0.750  1.383 
IL-1B rs1143627 TT Dom 0.809     
   CT Cod  1.037    
   TT Dom   1.472 1.135  
   TT Cod   1.113   
   CC<CT<TT Add     0.95 
   CC<CT<TT Add   1.202 1.060  
  rs16944 GG Dom 0.893     
   CC Dom    1.020  
   AA<AG<GG Add  1.128    
  rs1143634 CC Dom  0.831 0.847   
   CC Cod  0.952 0.987   
   TT<CT<CC Add   0.993   
   TT Rec  2.763 1.071 1.604 0.681 
   TT<CT<CC Add   0.747   
IL-2 rs2069762 GG Rec  0.278 0.329 0.795 0.904 
   GT Cod  1.153 1.014   
   GG<GT<TT Add   0.736  1.032 
IL-6 rs1800795 GG Dom  1.471    
   GG Cod  0.587   0.758 
   CC Rec    0.768  
   CG Cod  1.025    
   GG Dom  0.877    
   CC Rec   0.922   
IL-7R rs1494555 CT Cod   1.316   
   CC Rec  1.703    
   CT Cod  0.813  1.596 0.843 
   TT Dom   1.071   
   CC<CT<TT Add   1.166   
IL-10 rs1800871 TT Rec    0.702  
   CT Cod  3.351 1.374   
   CT Cod 1.124     
   TT Rec  1.019    
   TT<CT<CC Add    1.104  
  rs1800872 AC Cod   1.004   
   AA Rec  1.005    
  rs1800896 AG Cod 1.246     
   AG Cod 1.139     
   GG<AG<AA Add   0.794 0.953  
   GG Rec   1.419   
IL-17A rs8193036 CC Rec  1.397 2.395 1.329  
   CT Cod   0.919 0.903  
   CC<CT<TT Add  0.833    
   CC Rec .,419 2.677    
   CC<CT<TT Add    0.858  
   CT Cod  0.705    
  rs3819024 AA Dom   1.149 1.254  
   GG Cod   1.002   
   AG Cod   0.747   
   GG Rec  0.869    
   GG Rec 1.126 1.386    
  rs4711998 AG Cod   1.092 1.071  
   GG Cod    0.990  
   AA Rec   0.746   
   GG Dom    0.712  
   AA<AG<GG Add  0.837    
   GG Dom   0.894 0.674  
   AG Cod 1.377  1.013   
   GG Cod   0.918   
  rs2275913 AG Cod   1.340   
   AA<AG<GG Add     0.835 
   AA Rec  0.077   1.617 
   AA Rec  1.424   1.446 
IL-17F rs763780 TT Dom   0.723   
IL-23R rs6687620 TT Rec  0.539  8.736  
   CC Dom   0.564   
   TT<CT<CC Add     1.620 
   TT Rec  0.691 3.402 1.013 1.661 
   TT<CT<CC Add  1.358   0.639 
   CT Cod    0.514  
  rs11209026 GG Dom   0.495 0.994 1.740 
   AA<AG<GG Add   0.776 0.943  
   AA Rec   0.181  1.740 
   AG Cod   1.110   
   AA<AG<GG Add  1.030    
INF-γ rs2430561 AT Cod   1.537 0.806  
   AA<AT<TT Add  0.958    
   AA Rec  1.778    
   AT Cod  0.771    
  rs2069705 CT Cod   0.641 0.755 0.966 
   TT Rec     1.114 
   CT Dom 0.880     
   TT<CT<CC Cod  1.296    
   CT Add    0.810  
TGFβ rs2241716 AA<AG<GG Add  0.990    
   GG Dom  0.979    
   GG Dom 0.359 0.154    
   AA<AG<GG Add  0.667    
  rs1800469 TT Rec  1.566 3.320   
   CC Dom 0.875     
   TT<CT<CC Add 0.994     
   CT Cod 1.008 0.956    
   TT Rec   0.280 0.722  
   TT<CT<CC Add    1.101  
TNFα rs1799964 CC Rec 0.968     
   TT Dom   1.237   
   CC Rec  1.556    
   CC<CT<TT Add  0.939    
  rs1800629 AG Cod  2.820    
   AA Rec   0.938   
  rs1800610 AA Rec   0.807 0.632  
   AA<AG<GG Add   1.128   
   TT Rec   8.854   
   AA<AG<GG Add     1.139 
   TT Rec  0.751    

Add, additive; Cod, codominant; Dom, dominant; Rec, recessive.

Values (OR) for the variables were selected by the LASSO procedure (described in “Methods”).

*

OR values for the variables selected by the LASSO procedure (described in “Methods”). OR values were obtained with the exponential function of β coefficient, which compares the strength of the effect of each individual independent variable with the dependent variable. The higher the absolute value of β, the stronger the effect. β = 0, OR = 1 (neutral); β < 0, OR < 1 (L); β > 0, OR > 1 (H).

Table 5.

Multivariate analysis by LASSO of clinical and genetic variables and GVHD/NRM

VariablePolymorphismGenotypeD/RModel of transmissionOR*
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
Clinical          
 Recipient age >45 y       1.080  1.570 
 Recipient sex, male vs female        0.693 0.974 
 Female donor/male recipient         1.318 
 AML      0.858    
 ALL      1.614    
 MDS       1.260  2.374 
 Lymphoma      1.330    
 Myelofibrosis      0.640    
 MM      1.827    
 Conditioning, RIC vs MAC      0.679    
 Conditioning regimen, TBI vs no TBI      0.575    
 Stem-cell source, PB vs BM       1.557 2.281  
 aGVHD, grade 2-4       1.343 1.231  
Genetic          
 IL-1A rs1800587 TC Add     1.508 
  TT Rec  1.058    
 IL-1B rs1143627 TT Dom 0.809     
  CC<CT<TT Add   1.079 1.009  
 rs16944 GG Dom 0.893     
  AA<AG<GG Add  1.095    
 rs1143634 TT<CT<CC Add  0.927    
  TT Rec  2.968  1.016 0.664 
 IL-2 rs2069762 GT Cod     1.162 
  GG Rec  0.248 0.848 1.107 0.800 
  GT Cod  1.241 1.092   
 IL-6 rs1800795 GG Cod  1.065    
  GG Dom  1.664    
  CG Cod  1.557    
  CC<CG<GG Add  0.671    
  GG Cod  0.996    
  GG Dom  0.886    
 IL-7R rs1494555 CC Rec  2.007    
  CT Cod  0.792  1.161 0.845 
 IL-10 rs1800871 CT Cod  3.046    
  TT Rec  1.213    
  CT Cod 1.124     
  TT<CT<CC Add    1.002  
 rs1800872 AC Cod  1.088    
 rs1800896 AG Cod 1.246     
  AG Cod 1.139     
 IL-17A rs8193036 CC Rec  1.062 1.019   
  CC<CT<TT Add  0.642    
  CC Rec 1.419 2.274    
  CT Cod  0.611    
 rs3819024 GG Rec  0.797    
  GG Rec 1.126 1.222    
  GG<AG<AA Add  0.917   0.906 
 rs4711998 AG Cod 1.377     
  GG Dom    1.346  
  AA<AG<GG Add  0.894    
 rs2275913 AA Rec  0.085   1.267 
  AA<AG<GG Add     0.710 
  AA Rec  1.220    
  AG Cod  0.963    
 IL-23R rs6687620 TT<CT<CC Add   0.851   
  CT Cod     1.671 
  TT Rec  0.702  3.827  
  CC Dom   0.928   
  TT Rec  0.396    
  TT<CT<CC Add  1.267    
  CT Cod    0.752 0.647 
 rs11209026 GG Dom   0.983  1.896 
  AA<AG<GG Add   0.967   
  AA Rec     2.712 
 IFN-γ rs2430561 AT Cod    0.975  
  AA Rec  1.745    
  AT Cod  0.751    
 rs2069705 TT Rec     0.804 
  CT Cod   0.997   
  TT<CT<CC Add  0.929    
  CC Dom 0.880     
  CT Cod  1.304   1.162 
  TT Rec    1.017  
 TGFβ rs2241716 GG Dom 0.359 0.140    
  GG Cod  0.839    
  AA<AG<GG Add  0.581    
 rs1800469 TT Rec  1.201    
  CC Dom 0.875     
  TT<CT<CC Add 0.994     
  TT Rec   0.865 0.908  
  CT Cod 1.008     
 TNFα rs1799964 CC Rec 0.968     
  CC Rec  1.613    
  CC<CT<TT Add  0.930    
 rs1800629 AG Cod  3.422    
 rs361525 AG Cod  1.140    
  GG Dom  0.948    
VariablePolymorphismGenotypeD/RModel of transmissionOR*
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
Clinical          
 Recipient age >45 y       1.080  1.570 
 Recipient sex, male vs female        0.693 0.974 
 Female donor/male recipient         1.318 
 AML      0.858    
 ALL      1.614    
 MDS       1.260  2.374 
 Lymphoma      1.330    
 Myelofibrosis      0.640    
 MM      1.827    
 Conditioning, RIC vs MAC      0.679    
 Conditioning regimen, TBI vs no TBI      0.575    
 Stem-cell source, PB vs BM       1.557 2.281  
 aGVHD, grade 2-4       1.343 1.231  
Genetic          
 IL-1A rs1800587 TC Add     1.508 
  TT Rec  1.058    
 IL-1B rs1143627 TT Dom 0.809     
  CC<CT<TT Add   1.079 1.009  
 rs16944 GG Dom 0.893     
  AA<AG<GG Add  1.095    
 rs1143634 TT<CT<CC Add  0.927    
  TT Rec  2.968  1.016 0.664 
 IL-2 rs2069762 GT Cod     1.162 
  GG Rec  0.248 0.848 1.107 0.800 
  GT Cod  1.241 1.092   
 IL-6 rs1800795 GG Cod  1.065    
  GG Dom  1.664    
  CG Cod  1.557    
  CC<CG<GG Add  0.671    
  GG Cod  0.996    
  GG Dom  0.886    
 IL-7R rs1494555 CC Rec  2.007    
  CT Cod  0.792  1.161 0.845 
 IL-10 rs1800871 CT Cod  3.046    
  TT Rec  1.213    
  CT Cod 1.124     
  TT<CT<CC Add    1.002  
 rs1800872 AC Cod  1.088    
 rs1800896 AG Cod 1.246     
  AG Cod 1.139     
 IL-17A rs8193036 CC Rec  1.062 1.019   
  CC<CT<TT Add  0.642    
  CC Rec 1.419 2.274    
  CT Cod  0.611    
 rs3819024 GG Rec  0.797    
  GG Rec 1.126 1.222    
  GG<AG<AA Add  0.917   0.906 
 rs4711998 AG Cod 1.377     
  GG Dom    1.346  
  AA<AG<GG Add  0.894    
 rs2275913 AA Rec  0.085   1.267 
  AA<AG<GG Add     0.710 
  AA Rec  1.220    
  AG Cod  0.963    
 IL-23R rs6687620 TT<CT<CC Add   0.851   
  CT Cod     1.671 
  TT Rec  0.702  3.827  
  CC Dom   0.928   
  TT Rec  0.396    
  TT<CT<CC Add  1.267    
  CT Cod    0.752 0.647 
 rs11209026 GG Dom   0.983  1.896 
  AA<AG<GG Add   0.967   
  AA Rec     2.712 
 IFN-γ rs2430561 AT Cod    0.975  
  AA Rec  1.745    
  AT Cod  0.751    
 rs2069705 TT Rec     0.804 
  CT Cod   0.997   
  TT<CT<CC Add  0.929    
  CC Dom 0.880     
  CT Cod  1.304   1.162 
  TT Rec    1.017  
 TGFβ rs2241716 GG Dom 0.359 0.140    
  GG Cod  0.839    
  AA<AG<GG Add  0.581    
 rs1800469 TT Rec  1.201    
  CC Dom 0.875     
  TT<CT<CC Add 0.994     
  TT Rec   0.865 0.908  
  CT Cod 1.008     
 TNFα rs1799964 CC Rec 0.968     
  CC Rec  1.613    
  CC<CT<TT Add  0.930    
 rs1800629 AG Cod  3.422    
 rs361525 AG Cod  1.140    
  GG Dom  0.948    
*

OR values for the variables selected by the LASSO procedure (described in “Methods”). OR values were obtained with the exponential function of β coefficient, which compares the strength of the effect of each individual independent variable with the dependent variable. The higher the absolute value of β, the stronger the effect. β = 0, OR = 1 (neutral); β < 0, OR < 1 (L); β > 0, OR > 1 (H).

Table 6.

Summary of genetic variables selected by LASSO multivariate analysis and their influence on GVHD/NRM development compared with previous publications

Genetic variablePolymorphismGenotypeD/ROR*InflammatoryPhysiologyReference
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
IL-1A rs1800587 TT    Pro T/T genotype: increased promoter activity and IL-1A production, higher incidence of cGVHD 51,52  
IL-1B rs1143627 TT     Pro C allele: high levels of IL-1 53  
  CC<CT<TT     TT: higher risk of aGVHD 54  
 rs16944 CC      T allele: high levels of IL-1 53  
  TT<TC<CC        
 rs1143634 TT<CT<CC     T allele: high IL-1 production and more aGVHD; TT genotype: correlated with protector for infections 55,56-57  
  TT  H     
IL-2 rs2069762 GT     Pro GG: lower levels of IL-2 and lower GVHD 58,59  
  GG  L    
  GT    
IL-6 rs1800795 GG/GC     Pro Allele G: higher levels of IL-6, higher risk of aGVHD 60,61  
  GG       
IL-7R rs1494555 GG  H    Pro GG in donor: higher severe GVHD and NRM 62  
  GA   
IL-10 rs1800871 TC  H    Anti Allele C: high IL-10 production, Lower GVHD 63,64  
  TT/TC      
 rs1800872 AC      Allele C: high IL-10 production, lower GVHD  
 rs1800896 AG      Allele G: high IL-10 production, lower GVHD  
  AG        
IL-17A rs8193036 CC    Pro Genotype CC: higher GVHD 38  
  CC H       
  CT        
 rs2275913 AA  L    Allele A: correlated with higher levels of IL-17A and higher GVHD in donor 65,66  
 AA     
  AG        
 rs3819024 GG      Allele A: correlated with lower levels of IL-17A and lower GVHD  
  GG       
 rs4711998 GG/AG       
  AA<AG<GG        
IL-23R rs6687620 TT<CT<CC     Pro   
  CT        
  TT   H     
  TT/CT  L      
 rs11209026 GG      No correlation was obtained 67  
IFN-γ rs2430561 AT     Pro/anti Genotype TT: correlated with higher levels; T allele: correlated with moderate-severe GVHD 68,69  
  TT        
  AA        
  AT        
 rs2069705 TT<CT<CC      Genotype TT: correlated with higher levels; CC genotype: correlated with low incidence of cGVHD 70,71  
  CC        
  CT/TT      
TGFβ rs2241716 GG L L    Anti Genotype AA: high incidence of extensive cGVHD 71  
 rs1800469 CC      Allele C: correlated with higher probability to develop aGVHD and lower levels 71,72  
  CT        
  TT       
TNFα rs1799964 CC     Pro TT carriers: lower levels than CC genotype 73,74  
  CC        
 rs1800629 AG  H     AA genotype: correlated with higher levels 75,76  
 rs361525 AG      GG genotype: low production of TNFα; GA genotype: higher rates of cGVHD 77,78  
  GG        
Genetic variablePolymorphismGenotypeD/ROR*InflammatoryPhysiologyReference
aGVHD 2-4aGVHD 3-4cGVHDExtensive cGVHDNRM
IL-1A rs1800587 TT    Pro T/T genotype: increased promoter activity and IL-1A production, higher incidence of cGVHD 51,52  
IL-1B rs1143627 TT     Pro C allele: high levels of IL-1 53  
  CC<CT<TT     TT: higher risk of aGVHD 54  
 rs16944 CC      T allele: high levels of IL-1 53  
  TT<TC<CC        
 rs1143634 TT<CT<CC     T allele: high IL-1 production and more aGVHD; TT genotype: correlated with protector for infections 55,56-57  
  TT  H     
IL-2 rs2069762 GT     Pro GG: lower levels of IL-2 and lower GVHD 58,59  
  GG  L    
  GT    
IL-6 rs1800795 GG/GC     Pro Allele G: higher levels of IL-6, higher risk of aGVHD 60,61  
  GG       
IL-7R rs1494555 GG  H    Pro GG in donor: higher severe GVHD and NRM 62  
  GA   
IL-10 rs1800871 TC  H    Anti Allele C: high IL-10 production, Lower GVHD 63,64  
  TT/TC      
 rs1800872 AC      Allele C: high IL-10 production, lower GVHD  
 rs1800896 AG      Allele G: high IL-10 production, lower GVHD  
  AG        
IL-17A rs8193036 CC    Pro Genotype CC: higher GVHD 38  
  CC H       
  CT        
 rs2275913 AA  L    Allele A: correlated with higher levels of IL-17A and higher GVHD in donor 65,66  
 AA     
  AG        
 rs3819024 GG      Allele A: correlated with lower levels of IL-17A and lower GVHD  
  GG       
 rs4711998 GG/AG       
  AA<AG<GG        
IL-23R rs6687620 TT<CT<CC     Pro   
  CT        
  TT   H     
  TT/CT  L      
 rs11209026 GG      No correlation was obtained 67  
IFN-γ rs2430561 AT     Pro/anti Genotype TT: correlated with higher levels; T allele: correlated with moderate-severe GVHD 68,69  
  TT        
  AA        
  AT        
 rs2069705 TT<CT<CC      Genotype TT: correlated with higher levels; CC genotype: correlated with low incidence of cGVHD 70,71  
  CC        
  CT/TT      
TGFβ rs2241716 GG L L    Anti Genotype AA: high incidence of extensive cGVHD 71  
 rs1800469 CC      Allele C: correlated with higher probability to develop aGVHD and lower levels 71,72  
  CT        
  TT       
TNFα rs1799964 CC     Pro TT carriers: lower levels than CC genotype 73,74  
  CC        
 rs1800629 AG  H     AA genotype: correlated with higher levels 75,76  
 rs361525 AG      GG genotype: low production of TNFα; GA genotype: higher rates of cGVHD 77,78  
  GG        
*

OR values for the variables selected by the LASSO procedure (described in “Methods”). OR values were obtained with the exponential function of β coefficient, which compares the strength of the effect of each individual independent variable with the dependent variable. The higher the absolute value of β, the stronger the effect. β = 0, OR = 1 (neutral); β < 0, OR < 1 (L); β > 0, OR > 1 (H).

H, OR > 2; L, OR < 0.5.

Univariate analysis allowed us to identify statistically significant (P < .05) clinical variables and cytokine gene polymorphisms associated with the development of aGVHD, cGVHD, and NRM. Clinical variables seemed to have a reduced influence on the development of aGVHD. In contrast, IL-1B and IL-17A were the most important cytokines for the development of grade 2 to 4 aGVHD, as IL-6 was for grade 3 to 4 aGVHD. Clinical variables (age, conditioning regimen, stem-cell source, and previous development of aGVHD) seemed to have a greater influence on the development of cGVHD. Likewise, the most important cytokines were IL-1A, IL-1B, IL-23R, and INF-γ for cGVHD and IL-2, IL-17A, IL-23R, and TGFβ for extensive cGVHD. Only sex mismatch and IL-17A were associated with the occurrence of NRM (Table 2; supplemental Table 4).

The LASSO approach (supplemental Figure 1) allowed us to obtain the best models for predicting GVHD (aGVHD and cGVHD) and NRM. For anticipating grade 2 to 4 aGVHD, cGVHD, and NRM, none of the models was good enough for stratifying patients (Figure 1). For grade 3 to 4 aGVHD, the best C-M included 3 variables: conditioning, TBI, and disease (Table 3; Figure 1), which rendered a CCR1 of 50% and AUC of 0.6. The negative predictive value (NPV) of this model was 91.8%.

Figure 1.

Box plots of CCRs for patients who develop (CCR1) and do not develop (CCR0) GVHD/NRM as predicted with the different models. The AUC and the number of variables used are shown in each case. The predictive ability of each model, built using 85% of the samples (training set), was computed with the remaining 15% of the samples (test set). To evaluate the performance and predictive ability of each model, training and testing samples were randomly selected and the procedure repeated 100 times. The distribution of the CCR and AUC over the 100 iterations is shown by means of box plots. CCRs for the development of aGVHD, cGVHD, and NRM obtained using the predictive model including only clinical variables (upper panels), the model including only genetic variables (middle panels), and the model including both clinical and genetic variables (lower panels) are shown. Number of clinical/recipient SNPs/donor SNPs variables is indicated in parenthesis.

Figure 1.

Box plots of CCRs for patients who develop (CCR1) and do not develop (CCR0) GVHD/NRM as predicted with the different models. The AUC and the number of variables used are shown in each case. The predictive ability of each model, built using 85% of the samples (training set), was computed with the remaining 15% of the samples (test set). To evaluate the performance and predictive ability of each model, training and testing samples were randomly selected and the procedure repeated 100 times. The distribution of the CCR and AUC over the 100 iterations is shown by means of box plots. CCRs for the development of aGVHD, cGVHD, and NRM obtained using the predictive model including only clinical variables (upper panels), the model including only genetic variables (middle panels), and the model including both clinical and genetic variables (lower panels) are shown. Number of clinical/recipient SNPs/donor SNPs variables is indicated in parenthesis.

The best G-M included 10 cytokines (IL-1B, IL-2, IL-6, IL-7R, IL-10, IL-17A, IL-23R, INF-γ, TGFβ, and TNFα). This model obtained a CCR1 of 88%, with an AUC of 0.8 and NPV of 96% (Table 4; Figure 1). In contrast, the model that included both clinical and genetic variables retained the same clinical variables from C-M and added 11 cytokines (IL-1A, IL-1B, IL-2, IL-6, IL-7R, IL-10, IL-17A, IL-23R, INF-γ, TGFβ, and TNFα). This model obtained a CCR1 of 100%, AUC of 0.9, and NPV of 98.6% (Tables 5 and 6; Figure 1). Interestingly, 9 SNPs were selected by LASSO in both models (grades 2 to 4 and 3 to 4), in genes that could be relevant for aGVHD pathophysiology. However, 13 SNPs were selected only in the grade 3 to 4 model in genes that may be related to the severity of the complication.

The best clinical model for predicting extensive cGVHD included age, sex, stem-cell source, and previous aGVHD (Table 3; Figure 1), with a CCR1 of 66.7%, AUC of 0.7, and NPV of 82.9%. The best genetic model included 10 cytokines (IL-1B, IL-2, IL-6, IL-7R, IL-10, IL-17A, IL-23R, INF-γ, TGFβ, and TNFα). This model obtained a CCR1 of 80%, AUC of 0.8, and NPV of 81% (Table 4; Figure 1).

When both genetic and clinical variables were included, the same clinical variables persisted, and 8 cytokines were added (IL-1B, IL-2, IL-7R, IL-10, IL-17A, IL-23R, INF-γ, and TGFβ), improving the results of C-M, with a CCR1 of 80%, AUC of 0.8, and NPV of 85.1% (Tables 5 and 6; Figure 1).

A detailed explanation of the SNPs selected in CG-M in light of previously reported results is included in Table 6 and in supplemental material.

On the basis of the β results from LASSO, risk scores were calculated for aGVHD and cGVHD as well as for NRM. Patients were categorized into 2 groups: low risk (below the cutoff value) and high risk (above the cutoff). Final risk scores with C-M, G-M, and CG-M are summarized in supplemental Tables 5-7, respectively.

Overall, prediction of grade 3 to 4 aGVHD was significantly better using CG-M (P < .001) than using C-M or G-M (Figures 1 and 2). However, similar results were obtained when predicting extensive cGVHD with both CG-M and G-M, and both performed better than C-M (Figures 1 and 3). When NRM was considered, both C-M and CG-M performed better than G-M (supplemental Figure 5).

Figure 2.

Stratification of the whole cohort of patients (n = 263) according to the risk of developing acute GVHD. Risk was calculated using the proposed predictive model including clinical variables (upper panels), genetic variables (middle panels), or both clinical and genetic variables (lower panels). Cumulative incidence curves are shown for the development of grade 2 to 4 aGVHD (left panels) and grade 3 to 4 aGVHD (right panels). The cutoff used was 0.28 for grade 2 to 4 aGVHD and 0.11 for grade 3 to 4 aGVHD.

Figure 2.

Stratification of the whole cohort of patients (n = 263) according to the risk of developing acute GVHD. Risk was calculated using the proposed predictive model including clinical variables (upper panels), genetic variables (middle panels), or both clinical and genetic variables (lower panels). Cumulative incidence curves are shown for the development of grade 2 to 4 aGVHD (left panels) and grade 3 to 4 aGVHD (right panels). The cutoff used was 0.28 for grade 2 to 4 aGVHD and 0.11 for grade 3 to 4 aGVHD.

Figure 3.

Stratification of the whole cohort of patients (n = 201) according to the risk of developing chronic GVHD. Risk was calculated using the proposed predictive model including clinical variables (upper panels), genetic variables (middle panels), or both clinical and genetic variables (lower panels). Because the time of onset after transplantation was not available to build cumulative incidence curves, bar charts are shown for cGVHD (right panels) and extensive cGVHD (left panels). The cutoff used was 0.53 for cGVHD and 0.3 for extensive cGVHD.

Figure 3.

Stratification of the whole cohort of patients (n = 201) according to the risk of developing chronic GVHD. Risk was calculated using the proposed predictive model including clinical variables (upper panels), genetic variables (middle panels), or both clinical and genetic variables (lower panels). Because the time of onset after transplantation was not available to build cumulative incidence curves, bar charts are shown for cGVHD (right panels) and extensive cGVHD (left panels). The cutoff used was 0.53 for cGVHD and 0.3 for extensive cGVHD.

Also, we calculated GVHD risk scores for the patients most recently undergoing transplantation (2005-2012) to test the usefulness of the models in a subset of patients treated following current practices. Interestingly, the results obtained in this subgroup of patients were similar to those reported for the whole cohort (supplemental Figures 3 and 4).

Finally, we calculated the incidence of aGVHD also including censored patients (total, n = 359), which interestingly rendered similar results compared with those of the previous analysis (supplemental Figure 4). Incidence of cGVHD could not be calculated because of a lack of cGVHD onset time point information.

Discussion

Despite advances in the knowledge of the pathophysiology of GVHD during the last 2 decades, 30% to 50% of patients undergoing allo-SCT develop this complication,32  which leads to high morbidity, reduces quality of life, and is associated with a significantly higher risk of treatment-related mortality and poorer overall survival.3  Therefore, it is essential to identify biomarkers that can help to estimate the risk of GVHD. Biomarkers may also identify patients who will not respond to traditional treatments,33  making it possible to implement more stringent monitoring and specific preventive care or modify treatment. Moreover, the ability to anticipate the risk of subsequent morbidity and mortality could facilitate personalized treatment plans, including additional immunosuppressive therapies introduced early for high-risk patients or reduced-intensity approaches in low-risk patients.

Classically, GVHD has been estimated based almost entirely on the presence of clinical symptoms; indeed, over the last 15 years, several groups,2,11,13,15,16  including ours,12,14,34  have demonstrated that non-HLA SNPs can be used as biomarkers to anticipate GVHD. Although some of these reports identified individual SNPs, in most cases, no single SNP is sufficient for prognosis. Thus, the simultaneous use of several SNPs may increase specificity and predictability. In any case, there are currently no validated laboratory tests to predict the risk of GVHD or patient survival.

Given that this study was performed in a large and homogeneous cohort, our results suggest that SNPs in cytokine genes, in combination with clinical factors, could predict severe GVHD (grade 3-4 aGVHD and extensive cGVHD). One of the limitations of our retrospective study is that the end point of extensive cGVHD, which is no longer used in clinical practice, was considered because patients from 1997 were included. Therefore, current clinical applicability of such results is limited, and results must be validated in an independent study considering in-use end points of mild, moderate, and severe cGVHD.

As described before, the LASSO procedure autonomously selected clinical variables that were previously known to influence GVHD and NRM development, such as older patient age, peripheral blood as stem-cell source, female donor/male recipient, and previous aGVHD,8-10  confirming the robustness of the approach.

Other characteristics (reduced-intensity conditioning and not having received TBI), for which reported data were more controversial, were also selected by the LASSO procedure as associated with GVHD,10,32  probably because less intense regimens tend to be offered to older and more heavily treated patients. Gene variants have been shown to alter the expression or function of the proteins responsible for immune response,4  and there is growing evidence to support the importance of genetic variability (gene polymorphisms) for predicting the risk of GVHD in individual recipients. In the present study, the LASSO procedure selected polymorphisms in known cytokine genes such as IL1B,35,36 IL6,26 IL10,37 IL17A,38-40 IL23R,41 INFγ ,42 TGFβ ,43  and TNFα,44,45  which were confirmed to correlate with the risk of severe aGVHD, and polymorphisms in IL1B,36 IL2, IL7R, IL17A, IL23R, INFγ ,42  and TGFβ were found to play an important role in the risk of extensive cGVHD. Furthermore, LASSO identified an association between various genes (IL246,47  and IL7R48 ) and GVHD, which have remained controversial in the literature. Of note, the approach generates complex models to predict severe GVHD that include a high number of genetic variables, probably derived from the fact that GVHD is a complex entity with different phases and cell types involved.

The main strength of our study is the development of a predictive model that combines clinical and genetic variables in a large cohort of homogeneous patients undergoing the same type of transplantation (HLA-identical sibling donor). These findings may not apply to patients undergoing transplantation with unrelated or non–HLA-identical sibling donors. The inclusion of SNPs as markers, together with clinical variables in the risk model, significantly improves the CCRs of patients with severe aGVHD in comparison with the models based only on clinical or genetic data. In fact, the best model for the anticipation of severe aGVHD was CG-M, with a high CCR1 of 100%; CG-M and G-M performed similarly, with a CCR of 80%. These results demonstrate the clinical usefulness of including genetic variables, in addition to the available clinical variables, in the predictive models. Such models are of clinical utility because they consistently identify patients who will develop GVHD. In any case, it is also important to identify those patients who will not develop severe GVHD. Interestingly, CG-M provided an NPV of 98.6% for severe aGVHD and 85.1% for extensive cGVHD. In contrast, the NPVs obtained for the other models were slightly worse (severe aGVHD: C-M, 91% and G-M, 96%; extensive cGVHD: C-M, 82.6% and G-M, 81%).

Interestingly, the models proposed here can be applied by other centers using the mathematical formulas shown in supplemental Tables 5-7.

In light of these results, it could be argued that patients who are classified as high risk for the development of severe GVHD, mainly aGVHD, would still receive standard-of-care immunosuppression. However, patients classified as low risk according to the model, and who therefore will most probably not develop severe GVHD, could benefit from modification of immunosuppressive therapy, thus preserving the graft-versus-leukemia effect. This would be of special relevance in those patients with persistent minimal residual disease before transplantation.49  Of course, before making any recommendations, these findings must be validated in prospective studies with large cohorts to demonstrate their clinical utility.

As previously mentioned, it is clear that no single SNP is sufficient for prognosis, but the use of simultaneous SNPs may increase specificity and predictability. Thus, other authors have also developed GVHD predictive models. Kim et al22  proposed SNP-based risk models, also including clinical and genetic variables, associated with transplantation outcomes, which allowed stratification of patients in terms of overall survival, relapse-free survival, NRM, and aGVHD, but not cGVHD. Hartwell et al50  recently described an early-biomarker algorithm that predicted lethal GVHD and survival measuring 4 biomarkers (ST2, REG3a, TNFR1, and IL-2Rα) on plasma samples on day +7 after SCT in 1287 patients. This study included transplantations performed with various types of donors (unrelated or related), whereas ours included only HLA-identical transplantations. As in our study, this model was also capable of predicting the risk of severe GVHD after SCT before the onset of GVHD symptoms. Unlike our proposal, this algorithm only included the 4 biomarkers and did not consider clinical data. This approach could be combined in future studies with the 1 proposed here to further improve predictive models. Moreover, including genetic markers that help predict response to drugs could drive therapeutic interventions in the management of GVHD.33 

Our main goal for the future would be to improve the model to make it useful for all patients. To this end, we are currently using next-generation sequencing to search for new polymorphisms in immune response–related genes, minor histocompatibility antigen genes, drug metabolism genes, and innate immunity genes. In conclusion, although prospective validation studies should be performed to confirm these results, the present study suggests a risk model using donor and recipient SNP markers and clinical variables that improves GVHD risk stratification, allowing optimized clinical management of patients undergoing transplantation.

The full-text version of this article contains a data supplement.

Acknowledgments

The authors would like to thank the Centro Nacional de Genotipado for help with genotyping. They would also like to acknowledge the patients who participated in this study, as well as the staff of the Hematology Department, Hospital General Universitario Gregorio Marañón (Madrid, Spain), who made the study possible.

This study was partially supported by Ministry of Economy and Competitiveness ISCIII-FIS Grants PI08/1463, PI11/00708, PI14/01731, PI17/01880, and RD12/0036/0061 and cofinanced by the European Regional Development Fund from the European Commission, the “A way of making Europe” initiative, and grants from Fundación LAIR and Asociación Madrileña de Hematología y Hemoterapia.

Authorship

Contribution: C.M.-L., E.B., M.C.A.-M., J.L.D.-M., J.R., and I.B. were responsible for conception and design; N.S., B.M.-A., V.G., J.B.N., M.G., R.d.l.C., S.B., A.J.-V., I.E., C.V., A.S., D.S., M.K., J.G., Á.U.-I., C.S., D.G., and J.L.D.-M. provided patients and samples; C.M.-L., E.B., M.C.A.-M., A.P., R.L., and I.B. collected and assembled data; C.M.-L., E.B., M.C.A.-M., A.P., M.G.-R., R.L., J.M.B., P.B., J.L.D.-M., J.R., and I.B. were responsible for data analysis and interpretation; C.M.-L., E.B., M.C.A.-M., and I.B. wrote the manuscript; and all authors gave final approval of the manuscript and are accountable for all aspects of the work.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

A complete list of the members of the GVHD/Immunotherapy Committee of the Spanish Group for Hematopoietic Transplantation appears in “Appendix.”

Correspondence: Carolina Martínez-Laperche, Laboratorio Genética Hematólogica, Edif Oncología Pl−1, Servicio de Hematología, Hospital General Universitario Gregorio Marañón, C/Doctor Esquerdo 46, 28007 Madrid, Spain; e-mail: cmlaperchehgugm@gmail.com.

Appendix: study group members

The members of the GVHD/Immunotherapy Committee of the Spanish Group for Hematopoietic Transplantation are: C.M.-L., B.M.-A., J.B.N., M.G., R.d.l.C., S.B., I.E., C.V., A.S., D.S., M.K., J.G., P.B., Á.U.-I., C.S., D.G., J.L.D.-M., and I.B.

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Author notes

*

C.M.-L., E.B., and M.C.A.-M. contributed equally to this work.