A novel predictive approach for GVHD after allogeneic SCT based on clinical variables and cytokine gene polymorphisms

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 al^22,23  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.

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.

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.

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).

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 

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.

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.

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 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.

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).