Conditioning and GVHD prophylaxis

The conditioning regimens used in this study included two standard myeloablative regimens (busulfan+cyclophosphamide (BuCY) and total body irradiation+CY (TBI+CY)) and a sequential intensified regimen (fludarabine+Ara-C plus TBI+Cy+etoposide) as previously described [7, 11, 12]. Conditioning selection was based on disease type and status at transplantation. Generally, patients with acute myeloid leukemia in complete remission (CR) were given BuCY, those with acute lymphoblastic leukemia in CR were given TBI+CY, and those in non-CR were administered the intensified regimen. In addition, some high-risk patients received the intensified regimen [7, 11, 12].

Cyclosporin A (CsA) plus methotrexate (MTX) (on days+1,+3,+6) was administered to patients who underwent a matched sibling donor (MSD) transplant for GVHD prophylaxis. CsA+MTX+thymoglobulin (ATG; Genzyme, Cambridge) was used for patients who underwent a matched unrelated donor (MUD) transplant for GVHD. The CsA+MTX+ATG+mycophenolate (MMF) combination was administered to patients who underwent haploidentical donor (HID) transplant [7, 11, 12].

Infection prophylaxis and treatment

At our institutions, oral sulfamethoxazole and norfloxacin were administered to all patients for infection prophylaxis [11, 12]. Ganciclovir was administered for the prophylaxis or treatment of cytomegalovirus (CMV) infection, whereas acyclovir was used for other viruses. Antifungal agents were prescribed for prophylaxis against fungal infections. Fluconazole (0.3 g/day) or itraconazole (0.4 g/kg/day) was used until 60 days posttransplantation for patients with no history of invasive fungal infection (IFI), while those with a history of IFI were given voriconazole (0.4 g/day), itraconazole (0.4 g/day), caspofungin (50 mg/day), or ambisome (0.5–1 mg/kg/day) intravenously. Oral voriconazole or itraconazole was prescribed as a substitute for intravenous (i.v) treatment when the peripheral white blood cell count was greater than 2.0×10^9/L and discontinued after 90 days posttransplantation.

Among antibiotics, carbapenems (imipenem or meropenem) combined with amikacin were administered as first-line antibiotics for patients developing fever during neutropenia. Vancomycin or piperacillin/tazobactam was used as the second-line antibiotic treatment. Other antibiotics were administered variably and to a minority of patients. For example, tigecycline was occasionally used for bacterial infections caused after administration of noneffective second-line antibiotics.

Sequencing of 16S rRNA for fecal specimens

DNA from each stool specimen was extracted and purified, and the 16S rRNA gene between the V3 and V4 regions was amplified using PCR with modified universal bacterial primers, as described in our previous reports [7]. Microbiome DNA concentrations were detected using a qPCR assay, and sequencing was performed using the HiSeq2500 PE250 platform [7]. Sequencing data were screened and filtered according to quality and then aligned to the full-length 16S rRNA gene using the SILVA reference alignment as a template. Sequences were assembled into operational taxonomic units (OTUs) with 97% similarity [7].

Microbiota analysis

Intestinal microbiota diversity was evaluated using the inverse Simpson index, as well as the number of operational taxonomic units (OTUs) and Shannon index as previously described [4, 7, 13]. Linear discriminant analysis (LDA) effect size (LEfSe) was applied to identify different microbiota characteristics using LEfSe software; the threshold on the logarithmic LDA score for discriminative features was 2.0 [7]. Phylogenetic classification at the family level was calculated according to a naive Bayesian classification scheme and the Greengenes reference database [4, 7, 14]. In addition, a nonparametric test (Mann–Whitney) was performed to compare the statistical differences between groups.

Clinical metadata and survival prediction

All clinical data, including neutrophil recovery, were collected via retrospective review of clinical characteristics by individuals blinded to the microbiota of the participants. Neutrophil recovery was defined as an absolute neutrophil count was greater than 0.5×10^9/L posttransplantation for three consecutive days.

Area under receiver operating characteristic (ROC) curves (AUCs) from logistic regression analysis were used to evaluate the predictive performance of outcomes. The cutoff values of the inverse Simpson index and the selected bacteria were determined through AUC analysis of ROC curves for the predictive survival model by identifying the highest AUC, with a corresponding sensitivity and specificity. Based on the cutoff values for survival, a score of 0 or 1 was assigned for the inverse Simpson index and abundance of each microbial taxon (including Lachnospiraceae, Ruminococcaceae, Erysipelotrichaceae, and Enterobacteriaceae), where 0 represented a positive association with survival and 1 indicated a negative association. An accumulated intestinal microbiota (AIM) score was then generated by summing all the values (Table ​(Table1)1) [14–16]. Finally, the AIM score for predicting outcomes was determined by estimating the performance of the microbiota. The primary outcomes overall survival (OS) and disease-free survival (DFS) were analyzed; additionally, transplant-related mortality (TRM) was calculated.

Table 1

Predictive microbiota score for survival

Scoring system (relative abundance)	Score
Diversity (A)
≥2.805	0
<2.805	1
Lachnospiraceae (B)
≥1.280%	0
<1.280%	1
Ruminococcaceae (C)
≥0.005%	0
<0.005%	1
Erysipelotrichaceae (D)
≥0.026%	0
<0.026%	1
Enterobacteriaceae (E)
≥27.602%	1
<27.602%	0
AIM score*
High	4–5
Low	0–3

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^*Accumulated intestinal microbiota (AIM) score: A+B+C+D+E; diversity, reverse Simpson index

Intestinal microbiota characteristics at neutrophil recovery

Although no difference was found between the low- and high-score groups before transplantation (pr-conditioning) (OTUs, 61 vs. 62; Shannon index, 1.91 vs. 1.89; P=0.935 and 0.780, respectively, Fig. S1A and B), the number of OTUs and the Shannon index of microbiota diversity were higher in the low-score group than that in the high-score group (44.0 vs. 22.0, 1.83 vs. 0.66, respectively, both P<0.001, Fig. S2A and B). To investigate enrichment differences in microbiota between the two groups, heatmap and LEfSe analyses were performed. We focused on the abundant bacterial families and compared their relative abundances between the two groups. The heatmap shows the abundance and phylogenetic composition of each subject in the two groups (Fig. 3A). In Fig. 3A, the transition from gray to red indicates the increase in microbiota abundance. The microbial abundance in the stool was higher in the low-score group than that in the high-score group (Fig. 3A). LEfSe analysis demonstrated that the enriched bacteria were significantly different between the groups (Fig. 3B). Furthermore, Lachnospiraceae, Ruminococcaceae, Erysipelotrichaceae, Bacteroidaceae, Akkermansiaceae, and Streptococcaceae were enriched in the low-score group, whereas Enterobacteriaceae, Aeromonadaceae, Peptostreptococcaceae, Moraxellaceae, Pseudomonadaceae, Staphylococcaceae, Clostridiaceae, and Burkholderiaceae were enriched in the high-score group (Fig. 3B).

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Fig. 3

Differences of microbiota abundance and constitution at day 15 posttransplantation between the groups. A Heatmap of bacterial abundance in each group of patients. B Linear discriminant analysis (LDA) effect size (LEfSe) algorithm was used to identify the enriched bacterial family in each group

As expected, other bacteria were different between the low- and high-score groups, for example, Bacteroidaceae and Porphyromonadaceae (38.846 vs. 0.006%, 1.135 vs. 0.000%, respectively, all P<0.001), consistent with the results of LEfSe analysis, as shown in Fig. 3B. Taken together, patients with low and high scores showed significantly distinct intestinal microbiota.

We also explored the correlations between the single parameters of the AIM score (Fig. S3). The reverse Simpson index was positively associated with the abundance of Lachnospiraceae (r=0.60, P<0.001) and negatively associated with the abundance of Enterobacteriaceae (r=−0.42, P<0.001). The abundance of Enterobacteriaceae was negatively correlated with that of Lachnospiraceae and Ruminococcaceae (r=−0.32 and−0.26, respectively, both P<0.001, Fig. S3).

Survival and TRM

The median follow-up was 19.0 months (range: 1.9–36 months). Based solely on the reverse Simpson index and the abundance of the four bacterial families, the 3-year cumulative OS was significantly different between each group with high and low index (or abundance) (all P<0.001, Fig. S5A-E). The 3-year cumulative OS based on the AIM score posttransplantation was 88.5% (85.2–91.8%) and 43.9% (37.9–49.9%) for the low- and high-score groups, respectively (P<0.001, Fig. 4A). The 3-year cumulative DFS posttransplantation was 78.9% (75.1–82.7%) and 39.3% (33.2–45.4%) for the low- and high-score groups, respectively (P<0.001, Fig. 4B).

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Fig. 4

The accumulated intestinal microbiota (AIM) score and its association with survival. Both the 3-year cumulative overall survival (OS, A) and the disease-free survival (DFS, B) were longer in the low-score than those in the high-score group. C The 3-year cumulative transplant-related mortality (TRM) was considerably lower in the low-score group than that in the high-score group

A total of 53 patients died at a median of 7.0 months (range: 1.6–19.0 months) during follow-up, and the causes of death included TRM (n=33) and relapse (n=20). Of the 33 patients who died of TRM, infection (n=16, including 1 Epstein-Barr virus-associated posttransplant lymphoproliferative disorder) was the main cause of TRM. Other causes of death included aGVHD (n=7), cGVHD (n=3), intracranial hemorrhage (n=1), hepatic veno-occlusive disease (n=1), hemorrhagic cystitis (n=1), thrombotic microangiopathy (n=1), and multiple organ failure (n=2), but the cause of death of 1 patient was unknown. The distribution of reasons for death between the two groups is shown in Table S1. The cumulative TRM is shown in Fig. 4C. The 3-year cumulative TRM posttransplantation was 7.1% (4.6–9.6%) and 35.8% (29.8–41.8%) for the low- and high-score groups, respectively (P<0.001, Fig. 4C).

Risk factors for survival and TRM

In multivariate analysis (Table ​(Table3),3), III-IV aGVHD, high AIM score, poor risk, and non-CR status during transplantation were independent risk factors for OS (hazard ratio [HR]=4.95, 5.68, 1.62, and 1.84; 95% confidence interval [CI]: 2.60–9.42, 2.75–11.71, 1.14–2.31, and 1.00–3.38; P<0.001,<0.001,=0.007, and 0.048, respectively). No other factors, including patient age, female donor, 4/10–5/10 HLA-mismatched donor, β-lactam administration, or conditioning intensity, were significantly associated with OS in multivariate analysis (all P>0.05), although female donors, HLA-mismatched donors, and intensified conditioning were risk factors in univariate analysis (P=0.015, 0.019, and 0.008, respectively). For DFS, poor-risk, non-CR status during transplantation, grades III-IV aGVHD, and high AIM scores were the independent risk factors (HR=1.57, 2.23, 3.12, and 3.04; 95% CI: 1.18–2.10, 1.31–3.81, 1.74–5.60, and 1.74–5.31; P=0.002, 0.003,<0.001, and<0.001, respectively). For TRM, III-IV aGVHD and high AIM scores were the independent risk factors (HR=7.60 and 3.92; 95% CI: 3.43–16.83 and 1.57–9.79; P<0.001 and=0.003, respectively). Female donor, intensified conditioning, blood stream infection, and β-lactam administration were not identified as independent risk factors (all P>0.05), although they were associated with TRM in univariate analysis (P=0.002, 0.002, 0.015, and 0.009, respectively). Meanwhile, patient age, genetic risk, HLA mismatch, graft source, and administration of vancomycin were not associated with TRM (all P>0.05).

Table 3

Multivariate analysis of outcomes

Factors	TRM		OS		DFS
	Univariate	Multivariate (P (HR, 95% CI))	Univariate	Multivariate (P (HR, 95% CI))	Univariate	Multivariate (P (HR, 95% CI))
Gender
Female	0.652	-	0.659	-	0.463	-
Male
Patient age, years
<32	0.831	-	0.545	-	0.831	-
≥32
Donor gender
Female	0.002	0.100 (2.09, 0.87–5.05)	0.015	0.061 (1.87, 0.97–3.58)	0.226	-
Male
Genetics
Poor-risk	0.071	0.242 (1.30, 0.84–2.00)	0.003	0.007 (1.62, 1.14–2.31)	0.005	0.002 (1.57, 1.18–2.10)
Others
Disease status
Non-CR	0.138	0.439 (1.40, 0.59–3.32)	0.001	0.048 (1.84, 1.00–3.38)	<.001	0.003 (2.23, 1.31–3.81)
CR
HLA mismatched
4–5	0.216	-	0.019	0.315 (1.17, 0.86–1.61)	0.080	0.495 (1.10, 0.84–1.44)
2–3
0–1
Graft source
PBSC	0.383	-	0.303	-	0.555	-
BM+PBSC
Conditioning
Standard	0.002	0.236 (1.64, 0.72–3.69)	0.008	0.841 (1.06, 0.58–1.94)	0.069	0.679 (1.13, 0.65–1.96)
Intensified
Blood stream infection	0.015	0.315 (1.59, 0.64–3.94)	0.282	-	0.309	-
β-lactam	0.009	0.484 (1.86, 0.33–10.59)	0.098	0.468 (1.53, 0.48–4.87)	0.054	0.774 (1.18, 0.39–3.56)
Carbapenems	0.106	0.533 (1.53, 0.40–5.81)	0.191	0.561 (1.36, 0.48–3.85)	0.097	0.429 (1.49, 0.55–4.04)
Piperacillin/tazobactam	0.113	0.830 (1.10, 0.45–2.73)	0.136	0.615 (1.20, 0.60–2.40)	0.302	-
Cephalosporin	0.097	0.613 (1.42, 0.37–5.49)	0.490	-	0.153	0.818 (1.10, 0.48–2.55)
Vancomycin (i.v.)	0.480	-	0.936	-	0.883	-
Amikacin	0.232	-	0.851	-	0.643	-
III-IV aGVHD	<.001	<.001 (7.60, 3.43–16.83)	<.001	<.001 (4.95, 2.60–9.42)	<.001	<.001 (3.12, 1.74–5.60)
AIM score*
High (4–5)	<.001	.003 (3.92, 1.57–9.79)	<.001	<.001 (5.68, 2.75–11.71)	<.001	<.001 (3.04, 1.74–5.31)
Low (0–3)

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aGVHD, acute graft-versus-host disease; AIM score, accumulated intestinal microbiota score; BM, bone marrow; CI, confidence interval; CR, complete remission; DFS, disease-free survival; HR, hazard ratio; i.v., intravenous; OS, overall survival; PBSC, peripheral blood stem cell; TRM, transplant-related mortality

We thank Lizhi Zhou (PhD, Southern Medical University, Department of Biostatistics, Guangzhou, China) for statistical analysis and critical modification of the manuscript. We are grateful to Mrs. Zhensheng Dong (Beijing Genomics Institute, Shenzhen, China) for analysis of the sequencing data from stool samples as well as the staff of the Beijing Genomics Institute. We would like to thank Editage (www.editage.cn) for English language editing.