A gut microbiota score predicting acute graft-versus-host disease following myeloablative allogeneic hematopoietic stem cell transplantation

Abstract

Although studies have reported that intestinal microbiota are associated with acute graft-versus-host disease (aGVHD), they lacked a satisfactory method for predicting aGVHD. We collected stool and blood samples at day 15 posttransplant from 150 patients from two centers who underwent myeloablative conditioning allogeneic hematopoietic stem cell transplantation (allo-HSCT). Stool microbiota were detected by 16S ribosomal RNA gene sequencing; inflammatory factors and T lymphocytes were detected by multiplex immunoassays and flow cytometry, respectively. A gut microbiota score (GMS) from a LASSO (least absolute shrinkage and selection operator) model was developed and validated to predict aGVHD. In the discovery cohort, the GMS could predict II-IV aGVHD (area under the receiver operating characteristic [ROC] curve [AUC] = 0.904, P < .0001). Furthermore, the validation model was consistent with the discovery set (AUC = 0.887, P < .0001). Regulatory T/T-helper 17 (Treg/Th17) cells ratio in the low GMS subgroup was higher compared with the high GMS (P = .012), and the validation set is consistent with the discovery set (P = .003). In addition, high cytokine levels were associated with high GMS. In conclusion, the GMS at neutrophil engraftment could predict aGVHD, and it was a potential and novel method. The GMS was associated with the inflammatory factor and Treg/Th17 balance.

Keywords: graft survival; graft-versus-host disease (GVHD); graft-versus-leukemia (GVL)/graft versus tumor; hematology/oncology; immunosuppression/immune modulation; translational research/science.

Conflict of interest statement

The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

© 2019 The Authors. American Journal of Transplantation published by Wiley Periodicals, Inc. on behalf of The American Society of Transplantation and the American Society of Transplant Surgeons.

Figures

Figure 1

Diagram of patient groups enrolled in this study

Figure 2

Cumulative incidence of aGVHD. A, The cumulative incidence of grade II‐IV aGVHD by day +100 posttransplant was 42.2% and 41.7% for the discovery and validation cohorts, respectively (P = .926). B, The incidence of grade III‐IV aGVHD by day +100 posttransplant was 12.7% and 18.8% for the discovery and validation cohorts, respectively (P = .340) [Color figure can be viewed at https://www.wileyonlinelibrary.com]

Figure 3

Intestinal ecosystems of patients at engraftment. Intestinal ecosystems of patients with aGVHD and non‐aGVHD in the discovery set (n = 102) (A) and validation set (n = 48) (B). Each rank is a study subject, which represents the phylogenetic composition of each subject. The relative abundance of intestinal bacterial taxa is shown. aGVHD, acute graft‐versus‐host disease [Color figure can be viewed at https://www.wileyonlinelibrary.com]

Figure 4

The correlation heatmaps of intestinal microbiota at engraftment. The correlation heatmaps of relative abundance of 42 intestinal bacteria are shown in the discovery set (A) and the validation set (B). The blue color represents positive correlation (0~1) and the red color represents negative correlation (−1~0). The depth of the color is related to the power of correlation. The associations of relative abundance of 4 intestinal bacteria are shown [Color figure can be viewed at https://www.wileyonlinelibrary.com]

Figure 5

Tuning parameters for intestinal microbiota selection in the LASSO regression model. Feature selection and LASSO coefficient analysis of the 42 features of intestinal bacterial were performed. Based on 10‐fold cross‐validation via minimum criteria, the 20 coefficients were chosen as the vertical line presented in the plot. LASSO, least absolute shrinkage and selection operator [Color figure can be viewed at https://www.wileyonlinelibrary.com]

Figure 6

The GMS was associated with inverse Simpson index and could predict aGVHD. Inverse Simpson index was higher in the low GMS group compared with the high GMS group both in discovery set (P = .001, 4A) and validation set (P < .001, 4B). Receiver operating characteristic (ROC) curves show the predictive performance of GMS and inverse Simpson index for estimating grade II‐IV aGVHD (C and D). The results of the bootstrap (n = 2000) test for the two ROC curves indicate that the AUC of the sum of GMS was significantly higher than those of the inverse Simpson index both in discovery set (0.904 vs 0.622, P < .001) and validation set (0.887 vs 0.673, P = .005). Similarly, ROC curves of the GMS showed the predictive performance in the discovery and validation cohorts for estimating grade III‐IV aGVHD, respectively (AUC = 0.808 and 0.860; P < .001 and = .001, respectively; E and F). AUC, area under the ROC curve; GMS, gut microbiota score; ROC, receiver operating characteristic [Color figure can be viewed at https://www.wileyonlinelibrary.com]

Figure 7

Cumulative incidence of aGVHD according to the GMS. A, The cumulative incidence of grade II‐IV aGVHD by day +100 posttransplant was lower for the low GMS subgroup than for the high GMS subgroup in the discovery cohort (21.7% vs 84.8%, P < .001). B, The validation was consistent with the discovery cohort, the incidence of II‐IV aGVHD for the low and high GMS subgroups was 21.4% and 70.0%, respectively (P = .001). C, The incidence of III‐IV aGVHD was also lower for the low GMS subgroup than for the high GMS subgroup in the discovery cohort (4.3% vs 30.3%, P < .001). D, The incidence of III‐IV aGVHD in the validation cohort was still consistent with the discovery cohort for the low and high GMS subgroups (3.6% vs 40.0%, P = .002) [Color figure can be viewed at https://www.wileyonlinelibrary.com]

Figure 8

GMS was associated with T cell immune. The phenotype of regular T cells (Treg) (A) and IL‐17‐producing T cells Th17 (B) in peripheral blood mononuclear cells was examined by flow cytometric analysis. The percentages of Treg (C and D) and Th17 (E and F) cells were shown in the discovery and validation cohorts. The percentage of Treg cells was higher in the low GMS group than in the high GMS group both in the discovery set (P = .034) and validation set (P = .010). On the contrary, the percentage of Th17 cells was lower in the low GMS group than in the high GMS group both in the discovery set (P = .011) and validation set (P = .006). In addition, low GMS level was associated with high ratio of Treg/Th17 ratio (G and H) compared with the high GMS in the discovery set (P = .001, 4A) and validation set. GMS, Gut microbiota score [Color figure can be viewed at https://www.wileyonlinelibrary.com]

Figure 9

GMS was associated with cytokine levels in peripheral blood. The cytokine levels (IL‐1β, IL‐6, IL‐17A, and TNF‐α) were higher in the high GMS group than in the low GMS group both in the discovery set (P = .017, .01, .033, and .033, respectively) and validation set (P = .019, .012, .016, and .035, respectively). GMS, gut microbiota score; IL‐1β, interleukin‐1β; IL‐6, interleukin‐6; IL‐17A, interleukin‐17; TNF‐α, tumor necrosis factor‐α [Color figure can be viewed at https://www.wileyonlinelibrary.com]