Systematic, standardised pretransplant risk assessment is an important tool for predicting patient outcomes following allogeneic haematopoietic SCT (HSCT). To assess the European Group for Blood and Marrow Transplantation (EBMT) risk score capacities for predicting patient outcomes following unmanipulated haploidentical blood and marrow transplantation (HBMT), we analysed 502 leukaemia patients who received transplants at our centre between 2008 and 2010. The cohort OS and leukaemia-free survival (LFS) were 72.1% and 68.1%, whereas the cumulative non-relapse mortality (NRM) and relapse incidences were 16.5% and 16.1%. According to univariate analysis, the values for OS, LFS and NRM were worse for an EBMT risk score of 6 (40.0, 40.0, 50.0%) than a score of 1 (83.1, 78.3, 8.4%). Hazard ratios steadily increased for each additional score point. Likewise, a higher EBMT risk score was associated with an increased relapse incidence. Importantly, the EBMT risk score prognostic value regarding OS, LFS, NRM and relapse was maintained in the multivariate analysis. Moreover, we also made a haploidentical EBMT (haplo-EBMT) risk score, which used number of HLA disparity instead of donor type, and the haplo-EBMT risk scores can also be used to predict patient outcomes following unmanipulated HBMT.
Allogeneic haematological SCT (allo-HSCT) has become an integral part of therapy for leukaemia, because it provides a long-term cure for many patients.1,2 However, the presence of comorbidities and the risk of relapse remain obstacles to patient survival. Therefore careful pretransplant assessment of the potential risks is necessary and important.
The European Group for Blood and Marrow Transplantation (EBMT) risk score is a simple but important tool for predicting the outcomes of patients undergoing allo-HSCT.3 The EBMT risk score was based on an analysis of registry data of 3142 patients transplanted between 1989 and 1997 for CML in Europe.4 The score accounted for five parameters, that is, the recipient's age, disease stage, donor type, interval from diagnosis to transplantation and donor–recipient gender combination and was grouped from 0 to 7. Higher scoring groups are associated with decreased OS and leukaemia-free survival (LFS) times and increased non-relapse mortality (NRM).3,4 Notably, in recent years, many published studies show that the EBMT risk score is predictive for OS, NRM and relapse mortality in a variety of malignant and non-malignant haematological disorders, including acute leukaemia, myelodysplastic syndromes, lymphoma, multiple myeloma and aplastic anaemia, and these results are based on >50 000 allogeneic stem cell transplants in >10 years.3,5 However, the majority of the patients enrolled in these studies received allografts from HLA-identical siblings or unrelated donors;3,4 few of these patients received haploidentical transplantation. The approach to include haploidentical transplants in the ‘non-HLA identical sibling donor’ category is not precise, and the significance of the EBMT risk score is unclear in haploidentical transplantation. In our centre, we used a uniform regimen that includes the transplantation of G-CSF-mobilised PBSCs (G-PBSCs) and G-CSF-primed BM (G-BM) from HLA-mismatched/haploidentical related donors without in vitro T-cell depletion, and we name this transplant protocol as unmanipulated haploidentical blood and marrow transplantation (HBMT)6,7. This protocol was reliable for leukaemia patients with similar GVHD, NRM, relapse and survival.8,9,10,11,12,13,14 We wanted to determine whether the current EBMT risk score could be used to predict outcomes in unmanipulated HBMT patients, and the goal of this study was to attempt to answer this question by analysing the data from unmanipulated HBMT cases in our centre.
Subjects and methods
Consecutive leukaemia patients (n=502) who received allo-HSCT from HLA-mismatched family donors between January 2008 and December 2010 were enrolled in this study. Fifty-nine of the 502 patients had CML, 228 had AML, 211 had ALL and 4 patients had mixed phenotypic acute leukaemia. All protocols were approved by the institutional review board of the Peking University Institute of Haematology, and all patients and their donors signed consent forms. Other characteristics of the patients and donors are summarised in Table 1. The end point of the final follow-up was 31 June 2011.
Donor selection and HLA typing
Donor selection and HLA typing were previously described in detail.9 To determine the HLA-A and HLA-B status, low-resolution DNA techniques were used. High-resolution techniques were used for HLA-DRB1 typing. All donor–recipient pairs were typed at the HLA-A, -B and -DR loci at our institute. For each donor–recipient pair, the patient received stem cells from a family member who shared one HLA haplotype with the patient but who differed to some degree in the HLA-A, -B and -DR antigens of the haplotype that was not shared. In addition, HLA typing was performed on the parents and offspring of each donor–recipient pair to guarantee a true haploid genetic background among the pairs.
The conditioning therapy consisted of cytarabine (4 g/m2/day) administered intravenously on days −10 to −9, busulfan (3.2 mg/kg/day) administered intravenously on days −8 to −6, cyclophosphamide (1.8 g/m2/day) administered intravenously on days −5 to−4, semustine (250 mg/m2) administered orally on day −3 and anti-thymocyte globulin (2.5 mg/kg/day, rabbit (Sang Stat, Lyon, France)) administered intravenously on days −5 to −2. All transplantation recipients were given CsA, mycophenolate mofetil and short-term MTX for GVHD prophylaxis,8 and GVHD was treated in accordance with the common international criteria.15 The regimen of prevention, monitoring, intervention and treatment of relapse was in accordance with a previous study conducted at our centre.16,17,18,19,20
Calculation of the EBMT and haplo-EBMT risk scores
The EBMT risk score was calculated as described by Gratwohl et al.4 for patients with CML. One modification was made in patients with acute leukaemia; the parameter disease stage was scored as follows: CR1: 0 point, CR>1: 1 point, and advanced/active disease: 2 points. For unmanipulated HBMT at our centre, the donor type parameter was the same as 1. Similarly, we also calculate the significance of number of mismatched HLA-A, -B and DR loci, and we made a haplo-EBMT risk score with the parameters of the recipient's age, disease stage, interval from diagnosis to transplantation, donor–recipient gender combination and number of mismatched HLA-A, -B, and -DR loci. The parameter mismatched HLA-A, -B and -DR loci number was scored as follows: 1 disparity: 0 points, 2 disparity: 1 point, and 3 disparity: 2 points, and the haplo-EBMT risk score was grouped from 0 to 8. (Table 2)
The end point of the last follow-up for all surviving patients was 31 June 2011. Data were censored at the time of death or last available follow-up. Survival probabilities, including OS and LFS, were estimated using the Kaplan–Meier method. Competing risk analyses were used to calculate the cumulative incidence of relapse and NRM. NRM and relapse were competing events. Hazard ratios (HRs) for OS were estimated from the univariate and multivariate Cox regression analyses. The HRs for relapse and NRM were estimated from univariate and multivariate competing risk regression analyses. Factors included in the regression model were the leukaemia subtype, recipient's age, disease stage, donor type, interval from diagnosis to transplantation and donor–recipient gender combination. All of the factors with P<0.15 in the univariate analysis were included in a multivariate regression, and P<0.05 was considered statistically significant. All of the reported P-values were based on two-sided hypothesis tests. Data analyses were primarily conducted with the SPSS software (SPSS Inc., Chicago, IL, USA), and R software (version 2.6.1; http://www.r-project.org) was used for competing risk analysis.
Table 1 summarises the characteristics of the 502 patients enrolled in this study. After a median follow-up of 502 days (range 32–1274 days) for surviving patients, 342 patients (68.13%) were alive and in CR. However, 61 patients (12.15%) died from relapse, and 84 patients (16.73%) died from NRM. The OS after unmanipulated HBMT was 71.12%. The cumulative incidences of relapse and NRM after unmanipulated HBMT were 16.14% and 16.73%, respectively. A total of 318 (63.35%) patients had acute GVHD, and the 100-day cumulative incidence was 11.55% for grade III–IV acute GVHD. A total of 224 (44.62%) patients had chronic GVHD. Five hundred patients (99.60%) achieved sustained myeloid engraftment. The median time to reach an absolute neutrophil count >0.5 × 109 cells/L was 13 days (range 8–29 days). Four hundred seventy-seven patients (95.02%) achieved sustained platelet engraftment. The median time to reach a platelet count >20 × 109 cells/L was 16 days (range 5–195 days).
Assessment of outcome according to the EBMT risk score
The median EBMT risk score was 2 (0–7) points, with 16.5, 36.1, 26.7, 12.5, 6.2 and 2.0% of patients scoring 1, 2, 3, 4, 5 and 6 points (Table 1). As shown in Figure 1a, the projected OS rates of patients with EBMT risk scores of 1, 2, 3, 4, 5 and 6 points were 83.1, 77.3, 73.1, 57.1, 48.4 and 40.0% after unmanipulated HBMT (P<0.001). As shown in Figure 1b, the LFS rates of the patients with scores of 1, 2, 3, 4, 5 and 6 points were 78.3, 72.4, 70.1, 54.0, 45.2 and 40.0% after unmanipulated HBMT (P<0.001). As shown in Figure 1c, the cumulative incidences of NRM were 8.4, 13.3, 17.4, 27, 25.8 and 50% with scores from 1 to 6 (P<0.001). At the same risk scores, the cumulative incidences of relapse were 13.3, 14.9, 13.4, 20.6, 35.5 and 10.0% (P=0.004; Figure 1d).
Predicting OS, LFS, NRM and relapse in univariate and multivariate analyses
Next, we performed univariate analyses for a number of established risk factors using the Cox proportional hazard regression model to determine their impacts on OS, LFS, NRM and relapse (Table 3). By definition, the lowest risk category within the respective group was assigned an HR of 1. Then HRs with 95% confidence intervals (CIs) and P-values were calculated for both the overall trend and the comparison of each category with the lowest risk category. Here time from diagnosis to transplant (P=0.062), disease status (P<0.001), donor–recipient gender (P=0.013) and number of mismatched HLA-A, -B and -DR (P=0.096) were predictive for OS. Disease status (P<0.001) and donor–recipient gender (P=0.109) were prognostic for LFS. Whereas time from diagnosis to transplant (P=0.004), disease status (P<0.001), donor–recipient gender (P=0.003) and number of mismatched HLA-A, -B and -DR (P=0.085) were prognostic for NRM, time from diagnosis to transplant (P=0.135) and disease status (P<0.001) were associated with an increased incidence of relapse. Patient age was not predictive for OS, LFS, NRM or relapse in our cohort (P>0.15). In addition, the EBMT risk score was predictive for several outcomes: OS with a maximum HR of 5.58 (95% CI: 2.14–14.52) for patients with a score of 6 when compared with patients with a score of 1 (P<0.001), LFS with an HR of 4.07 (95% CI: 1.62–10.27) (P<0.001) and NRM with an HR of 8.91 (95% CI: 2.83–28.12) (P<0.001) for patients with a score of 6 compared with patients with a score of 1.
Finally, multivariate comparisons were performed (Table 4). Here disease status was highly predictive for OS (P<0.001), LFS (P<0.001), NRM (P=0.018) and relapse (P<0.001), and donor–recipient gender was associated with OS (P=0.022) and NRM (P=0.007), whereas time from diagnosis was associated with relapse (P=0.005). Likewise, the EBMT and haplo-EBMT risk score were both independent prognosticators for OS, LFS, NRM and relapse incidence in patients with higher score when compared with patients with lower score.
Several studies have investigated the association between the EBMT risk score and OS, LFS and NRM post-HSCT.21,22,23,24,25,26 However, the validity of the EBMT risk score for haploidentical transplantation has not previously been evaluated. To our knowledge, this study is the first to report the association between the EBMT risk score and different outcomes of transplantation in a group of unmanipulated HBMT recipients (n=502).
Our results show that patients with higher EBMT risk scores had worse outcomes, which is in accordance with previous studies. In our study, the EBMT risk score was associated with OS, LFS, NRM and relapse (Figure 1). Therefore, we suggest that the EBMT risk score might be a useful tool for assessing risk, guiding counselling and devising innovative therapies in patients who are in need of HSCT but who do not have HLA-identical sibling donors.
There are five factors in the EBMT risk score: stage of the disease, time from diagnosis, donor–recipient gender combination, age of the patients, and donor type. These five factors augment the risk for an individual patient with an increasing score from 0 as the best to 7 as the worst in an additive manner.3 The stage of the disease reflects the response to therapy and is an important risk factor that is widely used to predict the outcome after HSCT.23,27,28,29 For haploidentical HSCT, the significance of disease stage is also confirmed by other treatment centres in Europe.30,31 A >12-month duration of time from diagnosis to transplant means more treatment is required to achieve remission, which may add to the pretransplant toxicity and may increase NRM. However, this possibility is controversial in several studies. Terwey et al.22 and Hemmati et al.26 consider that longer duration of time from diagnosis to transplant to be the result of the stage of the disease for acute leukaemia patients. As a result, they created a modified EBMT risk score that omits this factor. In our study, 443 of the 502 patients were diagnosed as acute leukaemia, and a >12-month duration from diagnosis to transplant cannot predict outcomes in these patients (data not shown). Although we analysed both acute and chronic leukaemia patients together and found that a >12-month duration was related to worse OS and NRM in univariate analysis. Therefore we retain this variable in our study. The role of the donor–recipient gender combination was also significant. A female donor for a male recipient is always associated with a higher NRM, a lower relapse risk and a lower OS, which is in agreement with a previous study.14,27 Some studies suggested that maternal donors had better outcome due to tolerance induction from the passage of non-inherited maternal antigens,32,33 whereas in our study there were no statistical differences between maternal donors and other female donors for male recipients (data not shown). We observed that patient's age was not associated with outcomes after unmanipulated HBMT,14,27 and maybe it was because of the misdistribution of age at our centre. However, the patient's age was an important parameter in EBMT risk score, so we still analysed this score to include the parameter of patient's age. In haploidentical HSCT, the donor types were all same with score 1, so we analysed the relationship of HLA disparity and outcomes. The NRM was different among different groups according to the number of mismatched HLA-A, -B and -DR. One locus disparity had a lower NRM than two and three loci disparities, but there were no differences between the two and three HLA disparity group. Therefore, we assessed a haplo-EBMT risk factor score, which included stage of the disease, time from diagnosis, donor–recipient gender combination, age of the patients and number of mismatched HLA-A, -B and -DR.
This modified scale uses HLA-A, -B and -DR instead of donor type and is scored from 0 to 8. In our study, we found that the haplo-EBMT risk score was also associated with OS, LFS, NRM and relapse. After combining the haplo-EBMT risk score, we obtained three groups (0–2, 3–4, 5–8). These three groups were significantly different in terms of OS, LFS and NRM, and the relapse rate was higher for a score of 5–8 than for a score of 0–2. (Figure 2) The haplo-EBMT risk score was an independent prognosticator for OS, LFS, NRM and relapse rate in a multivariate analysis within the same time. Therefore, the haplo-EBMT risk score may be also a suitable prediction tool for HBMT without in vitro T-cell depletion at our centre.
However, there are still some limitations in this study. First, the EBMT/haplo-EBMT risk scores were significant for predicting the outcomes of HBMT without in vitro T-cell depletion at our centre, but this significance is unclear in other haploidentical HSCT modalities, such as T-cell depletion regimens. It is unknown whether this pretransplantation assessment method can extend to other haploidentical HSCT cases. Second, based on our previous study, the haematopoietic cell transplantation-specific comorbidity index (HCT-CI) can also predict outcomes after unmanipulated HBMT.34 Barba et al.35 suggest that combined HCT-CI and EBMT risk score can better stratify high-risk patients undergoing reduced toxicity allo-HSCT. In our transplantation system, whether the haplo-EBMT risk score and the HCT-CI can be combined to predict outcomes is still unknown, and the methods necessary to properly use these indexes to better stratify patients undergoing haploidentical HSCT require further research.
Moreover, there are still some predictive factors to be discussed. Currently, T-cell repleted (TCR) and T-cell depleted (TCD) HSCT represent two main transplant protocols in haploidentical transplant settings. There is no evidence indicating which protocol is better.36 Ciurea et al.37 compared TCR and TCD haploidentical HSCT and found that TCR protocol had a fast immune recovery. This result is in accordance with our data,7,38 suggesting that a rapid immune reconstitution may contribute to a superior outcome as reported by Ciurea et al.37 Interestingly, health-related quality of life in unmanipulated HBMT at our centre is comparable to that in patients receiving HLA-identical sibling allo-HSCT.39 Furthermore, other factors such as donor age can also be a useful predictive factor.32 Therefore, suitable usage of predictive tools to stratify patients for individual treatment needs further research.
In conclusion, the EBMT risk scores can predict outcomes of leukaemia after unmanipulated HBMT. The haplo-EBMT risk score can also be a pretransplant index for predicting the OS, LFS, NRM and relapse rates, and the modified haplo-EBMT score still requires validation in a larger, heterogenous cohort to be tested for prognostic implication.
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This work was supported in part by the Key Program of National Natural Science Foundation of China (Grant No. 81230013), Scientific Research Foundation for Capital Medicine Development (2011-4022-08), Chang Jiang Scholars Program, Beijing Municipal Science and Technology Commission (No. Z121107002612035), Beijing Municipal Science and Technology Commission (No. Z111107067311070) and Beijing Municipal Science and Technology Commission (No. Z121107002812033). We thank Rachel G and Cara B, medical editors at the American Journal Experts, for providing editing assistance to the authors during the preparation of this manuscript.
The authors declare no conflict of interest.
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