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Articles

Minimal residual disease prior to allogeneic hematopoietic cell transplantation in acute myeloid leukemia: a meta-analysis

Hematology/Oncology Fellowship Program, University of Washington, Seattle, WA, USA
Division of Hematopathology, Department of Laboratory Medicine, University of Washington, Seattle, WA, USA
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
Division of Hematopathology, Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
Biostatistics and Bioinformatics Core, Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
Unit of Blood Diseases and Stem Cell Transplantation, University of Brescia, A.O. Spedali Civili, Italy
Anthony Nolan Research Institute, London, UK;Royal Marsden Hospital, London, UK
Institute of Haematology and Blood Transfusion, Prague, Czech Republic
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA;Experimental Transplantation and Immunology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
Department of Pediatrics, Jena University Hospital, Germany
Department of Hematology, Fondazione Policlinico Tor Vergata, Rome, Italy
Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, WA, USA;Department of Health Services, University of Washington, Seattle, WA, USA;Department of Biomedical Informatics, University of Washington, Seattle, WA, USA
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA;Department of Medicine, Division of Hematology, University of Washington, Seattle, WA, USA;Department of Epidemiology, University of Washington, Seattle, WA, USA
Vol. 102 No. 5 (2017): May, 2017 https://doi.org/10.3324/haematol.2016.159343

Abstract

Minimal residual disease prior to allogeneic hematopoietic cell transplantation has been associated with increased risk of relapse and death in patients with acute myeloid leukemia, but detection methodologies and results vary widely. We performed a systematic review and meta-analysis evaluating the prognostic role of minimal residual disease detected by polymerase chain reaction or multiparametric flow cytometry before transplant. We identified 19 articles published between January 2005 and June 2016 and extracted hazard ratios for leukemia-free survival, overall survival, and cumulative incidences of relapse and non-relapse mortality. Pre-transplant minimal residual disease was associated with worse leukemia-free survival (hazard ratio=2.76 [1.90–4.00]), overall survival (hazard ratio=2.36 [1.73–3.22]), and cumulative incidence of relapse (hazard ratio=3.65 [2.53–5.27]), but not non-relapse mortality (hazard ratio=1.12 [0.81–1.55]). These associations held regardless of detection method, conditioning intensity, and patient age. Adverse cytogenetics was not an independent risk factor for death or relapse. There was more heterogeneity among studies using flow cytometry-based than WT1 polymerase chain reaction-based detection (I2=75.1% vs. <0.1% for leukemia-free survival, 67.8% vs. <0.1% for overall survival, and 22.1% vs. <0.1% for cumulative incidence of relapse). These results demonstrate a strong relationship between pre-transplant minimal residual disease and post-transplant relapse and survival. Outcome heterogeneity among studies using flow-based methods may underscore site-specific methodological differences or differences in test performance and interpretation.

Introduction

Morphologic complete remission (CR), defined by the presence of <5% bone marrow blasts and recovery of peripheral blood counts, is the long-standing standard for response assessment in acute myeloid leukemia (AML).51 Based on estimates of normal marrow cellularity,6 however, this cutoff allows for the presence of up to 1010 leukemic blasts or more. It is therefore not surprising that relapse remains the major cause of treatment failure among patients who have achieved a morphologic CR.54 Significant effort has gone into developing tools to identify minimal (or, perhaps more appropriately, measurable) residual disease (MRD), including multi-parametric flow cytometry (MFC) to enumerate myeloid cell populations with immunophenotypic abnormalities, polymerase chain reaction (PCR) to quantify leukemia-associated mutations or RNA transcript levels, and cytogenetic / fluorescence in situ hybridization to detect chromosome level changes specific to the malignant clone. Among these modalities, MFC- and PCR-based approaches have the highest sensitivity and are increasingly employed in the clinic.127

A large number of studies has demonstrated worse outcomes for patients who have MRD compared to similarly treated patients in whom no MRD can be detected. This relationship has been observed during/after induction and post-remission chemotherapy courses as well as before and after hematopoietic cell transplantation (HCT).127 The magnitude of the association between MRD status and risk of relapse varies widely between studies, however, as do the details of the detection methods. In addition to differences in the specifics of the MRD techniques across institutions, there are also differences in cut-points chosen to define MRD positivity, the patient material that is used to perform the MRD assay on (i.e., peripheral blood or bone marrow), and the timing as well as frequency with which MRD assessments are obtained. In this meta-analysis, we focused on MRD assessed immediately before allogeneic HCT in patients with AML, other than acute promyelocytic leukemia (APL). Besides ascertaining the relationship between pre-HCT MRD and post-transplant outcomes, we also investigated whether, and to what degree, the prognostic role of MRD is influenced by the method of MRD detection.

Methods

We searched PubMed/MEDLINE and EMBASE (Online Supplementary Table S1) for English language articles published between January 2005 and June 2016 that reported on the association between pre-HCT MRD (by PCR and/or MFC) and post-HCT survival in patients with non-APL AML in morphologic CR. Two authors (S.A.B. and R.B.W.) independently reviewed the search results. We excluded studies with <15 patients or <6 months of follow up. If needed, authors of included studies were contacted for additional information. Our search yielded 344 reports, which were screened according to 2009 PRISMA Guidelines (Figure 1). For studies of interest, we collected data on the number of patients, median/range age, median follow-up time, percentage of patients with adverse-risk cytogenetics (using the classification criteria reported by study authors), percentage of patients receiving myeloablative (MA) vs. reduced intensity conditioning (RIC), interval between MRD detection and HCT, and details of the MRD detection method. We assessed risk of bias using an instrument based on the Quality in Prognostic Studies (QUIPS),13 modified to reflect our judgment about potential biases (Online Supplementary Table S2). Finally, we obtained data for leukemia-free survival (LFS), overall survival (OS), and cumulative incidence of relapse (CIR) and non-relapse mortality (NRM) from the date of HCT. We used a hierarchical approach14 to compare outcomes of MRD and MRD subjects: (i) when available, we used observed hazard ratios (HRs) and confidence intervals (CIs); (ii) when Kaplan-Meier curves were provided, we used Enguage Digitizer version 4.1 to calculate HRs and CIs based on an established algorithm,15 and (iii) for articles providing survival data at single time points, we estimated HRs based on exponential decay.

Figure 1.PRISMA flow diagram for study selection. HCT: hematopoietic cell transplantation; MRD: minimal residual disease.

We performed a random-effects meta-analysis, with inter-study heterogeneity described using the I statistic16 (STATA version 14; StataCorp, College Station, TX, USA). Cut-points between MRD positivity and negativity were based on criteria specified by the individual publications. In one,17 no cut-point was specified for Wilms Tumor 1 (WT1) transcript level. As other studies used cut-points in the range of 50–70 copies/10 reference gene copies,2018 and as no events were observed at WT1 levels <65, a cutoff of 70 was used. In another study18 that used a WT1 cutoff of 50, there were no relapses in the MRD group (n=25) by 6.6 years. As no HR could be calculated, this study was not incorporated into pooled CIR results. In two studies in which HRs were extracted from survival curves,2221 curves were portrayed for subgroups within MRD and MRD patients; here, a weighted average of the HR between groups by number of patients per group was used to obtain a final HR. In one study19 reporting results by MFC and by WT1 PCR, we used MFC results for overall analysis, as these data were more complete.

Subgroup analyses involved stratification by MRD detection method, age, and conditioning intensity. We calculated the ratio of the percentage of patients with adverse cytogenetics in the MRD and MRD groups. If HRs for survival outcomes were higher in studies where this ratio was greater, it would indicate that adverse cytogenetics might be an independent negative prognostic factor.23 We used meta-regression to test this hypothesis.

Results

Included studies

Our search yielded 19 unique publications with a total of 1,431 patients (Table 1).372422211917 Details of transplant and conditioning regimens are shown in the Online Supplementary Table S3. The sole method of MRD detection was MFC in 9 studies37363329262422 and WT1 PCR in 5,32301817 while one study reported results separately for MFC- and WT1 PCR-based detection.19 Four studies used combination methods;35342521 all of these included MFC, and 3 also included PCR-based detection. Among studies using MFC-based detection, the cut-point between MRD positivity and negativity was fairly uniform: 11 of 14 used the limit of detection for the assay (around 0.1%), while 3 specified a cutoff of 0.1%,363326 which corresponded roughly to the limit of detection in these cases. In other words, heterogeneity in cut-points was primarily determined by differences in performance characteristics and interpretation of the assay rather than the cut-points selected. Among studies that only used PCR-based methods, all assessed quantitative PCR for WT1, while one study31 utilized a panel of other genes in addition to WT1. Two studies, both using combination approaches for MRD detection, targeted PCR at AML-specific mutations (e.g., Fms related tyrosine kinase 3 internal tandem duplication [FLT3/ITD])21 or fusions genes (e.g., RUNX1/RUNX1T1)25 present at diagnosis. Among studies quantifying WT1 transcript levels, most normalized against expression of ABL1; MRD cutoff levels varied between 50–70 copies of WT1 per 10 copies of ABL1.34321917

Table 1.Characteristics of included studies.

Five studies were considered as having a high risk of bias: the MRD measurement technique was implicated in all cases, and study confounding was felt possible in 2 of these cases (Figure 2). For 11 studies, we were able to obtain HRs for all reported outcomes from the manuscript or personal communication; for the other 8 studies, HRs were extrapolated from Kaplan-Meier curves or survival point estimates (n=4).36342618 MRD was measured within 60 days of HCT in all studies in which this information was reported, and within 30 days in all but one study.35

Figure 2.Risk of bias assessment illustrating review authors’ judgments about each risk of bias item for each included study.

Association between pre-HCT MRD status and post-HCT outcomes

Overall, MRD positivity was associated with worse LFS (HR=2.76 [1.90–4.00], I=70.0%), OS (HR=2.36 [1.73–3.22], I=59.7%), and CIR (HR=3.65 [2.53–5.27], I=37.9%) but not NRM (HR=1.12 [0.81–1.55], I<0.1%). After removing studies with a high risk of bias in any domain, MRD remained strongly associated with worse LFS (HR=3.24 [2.17–4.83], I=64.5%), OS (HR=2.64 [1.87–3.72], I=57.8%), and CIR (HR=4.06 [2.70–6.12], I=48.0%) while, again, there was no statistically significant association with NRM (HR=1.18 [0.80–1.75] I=0.9%).

Effect of MRD detection method on post-HCT outcomes

In subgroup analyses, being MRD was associated with an increased risk of relapse and mortality regardless of the detection method (Table 2). For CIR, the HR for WT1 PCR-based methods was statistically significantly larger than for MFC-based methods. Figure 3 shows a forest plot for the 17 studies reporting on the primary outcome of LFS, while similar plots for OS, CIR, and NRM can be found in the Online Supplementary Figures S1–S3. Results for studies using MFC-based methods were more heterogeneous than those using WT1 PCR or combination methods for LFS (I=75.1% vs. <0.1% and 57.2%), OS (I=67.8% vs. <0.1% and 12.5%), and CIR (I=22.1% vs. <0.1% and 6.7%). After excluding studies with a high risk of bias in any domain, WT1 PCR-based studies and combination methods continued to have low heterogeneity for LFS, OS, and CIR (all I<0.1%), whereas MFC-based studies showed persistent and considerable heterogeneity for LFS (I=81.5%), OS (I=73.8%), and CIR (I=46.4%).

Table 2.Pooled HRs [95% CI] and inter-study heterogeneity for all studies (above) and excluding a high risk of bias (below).

Figure 3.Forest plot showing hazard ratio (effect size, ES) for leukemia-free survival with pooling of results for each minimal residual disease detection method. Columns indicate study size (N) and whether each study carries a high risk of bias (Bias Risk). Within groups, studies are listed by year of publication. CI: confidence interval; MFC: multi-parametric flow cytometry; PCR: polymerase chain reaction; MRD: minimal residual disease.

While all MFC-based studies analyzed bone marrow tissue, WT1 PCR-based studies were mixed between the use of marrow and peripheral blood for analysis. Restriction to studies that reported data from peripheral blood323118 yielded essentially identical results. Outcomes for MFC-based studies were similar regardless of whether residual disease was detected via gating for the original leukemia-associated immunophenotype or based on detecting a phenotype different from normal, although results for the latter were more heterogeneous (I 89.7% vs. 32.5% for LFS, 88.3% vs. 0.0% for OS, and 70.8% vs. 0.0% for CIR). There were no significant differences in outcomes between MFC-based studies by number of fluorochromes (<6 vs. ≥6) used.

Effect of patient age on post-HCT outcomes

On subgroup analysis of age 0–20,3630262518 21–40,343128 and >40,35333229272422211917 we found no difference in the effect of MRD between groups. The same was true after exclusion of studies with a high risk of bias. Among studies reporting on older patients, there was sufficient data to further stratify into ages 40–60 and >60 for the LFS endpoint; the HR for this outcome was similar in these subgroups (HR=2.67 [1.46–4.86], I=81.1%; HR=3.02 [0.90–10.08], I=52.3%, respectively). When we restricted our analysis to studies using primarily MA conditioning, 2 were primarily pediatric (median age 0–20),3025 4 involved young adults (median age 20–40),34312827 and 5 involved older adults (median age >40).3532242119 The association between MRD and LFS was similar in all age groups, though between-study heterogeneity was high (age 0–20: HR=3.45 [0.39–30.86], I=89.5%; age 20–40: HR=2.35 [1.10–5.02], I=72%; age >40: HR=3.56 [1.79–7.05], I=77.5%).

Effect of conditioning intensity on post-HCT outcomes

Next, we considered whether differences in conditioning regimen intensity might explain between-study heterogeneity, particularly in light of conflicting results from Ustun et al.27 showing in a large cohort (n=203) that MA conditioning could compensate for the increased hazard for relapse and mortality associated with being MRD, while Walter et al.29 showed no such effect in 241 patients. Among studies reporting LFS as an outcome, 14 reported on the fraction of MRD and MRD patients who underwent MA versus RIC HCT. To test whether higher intensity transplant might reduce the negative impact of being MRD, we specifically analyzed studies in which >75% of MRD and MRD patients received MA HCT (n=12 for LFS endpoint), and compared the results with studies where 0% of patients received MA HCT (n=3 for LFS endpoint). Results from Ustun et al.27 were reported separately for MA and RIC patients within their publication, and for the purposes of this analysis, we treated these sets of results as two separate studies. As shown in Table 2 and as a forest plot in the Online Supplementary Figure S4, there was no indication that MA conditioning was able to attenuate the negative effects associated with MRD positivity on LFS, OS, or CIR. In contrast, the HRs for MA studies were numerically higher than for the few RIC studies, although the large confidence intervals exclude a definitive conclusion as to whether conditioning intensity affects the association between MRD status and post-HCT outcomes. The exclusion of high-risk studies did not fundamentally change these results and conclusions. Not surprisingly, all studies using RIC conditioning involved older adults (the >40 age group as stratified above).

Effect of cytogenetic risk on post-HCT outcomes

Most studies reporting cytogenetics in MRD and MRD patients used the Southwest Oncology Group3332282717 or 2010 Medical Research Council criteria,35292421 while one incorporated mutational profiling.34 The ratio of the proportion of adverse-risk cytogenetics among MRD to MRD patients ranged from roughly equal to 7.5 times higher in the MRD group. We used meta-regression to measure how HRs for LFS changed with variations in this risk ratio and found that differences in adverse-risk cytogenetics between MRD and MRD groups did not account for a significant proportion of between-study variance (R): R −9.15% (P=0.82) for all studies, and 14.83% (P=0.92) after excluding high-risk studies (Figure 4). Results were similar when the study with the highest ratio of 7.5 was excluded from this analysis (P=0.62). Similarly, adverse-risk cytogenetics was not an independent prognostic factor for OS (P=0.11), CIR (P=0.85), or NRM (P=0.99).

Figure 4.Figure 4. Meta-regression analysis showing the effect of the ratio of the percentage of MRDpos patients with adverse cytogenetics to the percentage of MRDneg patients with adverse cytogenetics on log-hazard for leukemia-free survival. A flat line indicates no relationship, and this is shown for all studies (A) and after excluding studies with a high risk of bias (B). MRD: minimal residual disease; HR: hazard ratio.

Testing for publication bias

Funnel plot analyses for each survival outcome are shown in Figure 5 as a graph of log-HR versus the variance in the log-HR. These plots did not suggest a publication bias, although they indicated that the publication of studies considered to have a high risk of bias could bias overall study results towards the null for LFS, OS, and CIR.

Figure 5.Funnel plot analysis for survival outcomes. Shown are (A) leukemia-free survival (LFS), (B) overall survival (OS), (C) cumulative incidence of relapse (CIR), (D) non-relapse mortality (NRM). HR: hazard ratio.

Discussion

The findings from this meta-analysis support our main conclusion that the presence of MRD before allogeneic HCT identifies patients at a higher risk of relapse and shorter survival relative to patients in whom no evidence of MRD is found. Although we were unable to incorporate results from one study with an incalculable HR for CIR (based on the lack of relapses among MRD patients), the findings from that report similarly supported our conclusion. The association between MRD and post-HCT relapse and mortality is robust, and is seen within all patient ages and regardless of which detection method is used. It is similarly found in those undergoing MA conditioning as well as RIC transplants without discernible difference in strength of association between these cohorts, suggesting that higher conditioning regimen intensity may not be able to overcome the adverse impact of MRD. To the extent that we were able to control for differences in cytogenetic risk with meta-regression, the negative impact of being MRD superseded any potential adverse effects of having poor-risk cytogenetics. In comparison, our analysis indicates that pre-HCT MRD is not associated with a significantly increased risk of NRM, in line with the notion that the association between pre-HCT MRD and OS is entirely accounted for by disease relapse without significant contribution from HCT toxicity.

Although our meta-analysis demonstrates a significant association between pre-HCT MRD status and post-HCT outcomes with both WT1 PCR- and MFC-based assays, we found a greater degree of heterogeneity in survival estimates in studies with MFC-based detection methods. This heterogeneity could not be accounted for by differences in patient age, conditioning regimen intensity, or cytogenetic risk. In addition, the cut-points between MRD positivity and negativity were primarily determined by the limits of detection of each particular assay, indicating that chosen cut-points are unlikely to account for heterogeneity. We were, however, able to show that at least some of this heterogeneity may be accounted for by study-specific differences in approach to MFC, with studies detecting residual disease based on initial leukemia-associated immunophenotypes having more uniform results than those using a “different from normal” approach. Other possible causes of heterogeneity might include site-specific differences in MFC methodology, including differences in antigens and fluorochromes used, methods for cell lysis, number of events collected, or specifics of the aspirate used for analysis, with an increasing risk of hemodilution with each pull. If such speculation is correct, efforts towards standardization/harmonization of MRD methods; as pioneered in acute lymphoblastic leukemia38 and currently underway for AML; might ultimately lead to less heterogeneous data with MFC-based MRD assays. In contrast, despite some heterogeneity in PCR targets and cut-points, PCR methodology may be relatively more standardized, accounting for more uniform results. As an illustration, the risk estimates for being MRD by PCR-quantified WT1 transcript levels are very similar across several studies, indicating that this method yields highly reproducible results for pre-HCT risk stratification. Even in the smallest studies,1917 in which there was no statistically significant relationship between MRD and LFS, observed HRs were consistent with the other, larger studies. One might wonder whether using more than one method to detect MRD might lead to more sensitive detection and stronger associations with relapse and survival, indicated by higher HRs. We found that studies using combination methods of MRD detection did not show stronger associations with survival outcomes over studies using either MFC- or WT1 PCR-based methods. That said, all four of these ‘combinations’ involved MFC-based detection, and the heterogeneity within the combination group may simply underscore the heterogeneity in MFC-based studies as a whole. Alternatively, MFC and WT1 PCR are both potentially highly sensitive tests, and using multiple modalities may not add much additional sensitivity in detection, or increases in assay sensitivity beyond current limits may not lead to appreciably stronger associations with survival outcomes.

Although our studies highlight the importance of pre-HCT MRD, we were unable to account for inter-study differences in the selection of patients for HCT, which may impact post-HCT results. It is conceivable that different strategies in allocating patients to different post-remission treatment strategies could affect our study results. Given the nature of our analysis, we were only able to test the effects of select covariates and only in an aggregate fashion. Similarly, we were not able to control for the considerable heterogeneity in transplant conditioning regimens, donor sources, graft characteristics, and immunosuppression, all of which could potentially influence relapse and death. Due to absent individual patient data, we were not able to assess whether higher levels of MRD were associated with higher risk of relapse. Regardless of these limitations, our results demonstrate a strong relationship between pre-HCT MRD status and post-HCT relapse and survival, but not NRM. Further studies are needed to determine how pre-HCT MRD status should guide therapeutic decisions, either through treatment intensification for MRD patients, or possibly de-intensification for patients who are found to be MRD by a reliable method.

Acknowledgements

SAB is supported by a fellowship training grant from the National Heart, Lung, and Blood Institute/National Institutes of Health (NHLBI/NIH: T32-HL007093). R.B.W. is a Leukemia & Lymphoma Society Scholar in Clinical Research. This work was supported in part by the Intramural Research Programs of the National Heart, Lung, and Blood Institute and the National Cancer Institute of the National Institutes of Health.

Footnotes

  • Received November 2, 2016.
  • Accepted January 20, 2017.

References

  1. Bisel HF. Criteria for the evaluation of response to treatment in acute leukemia. Blood. 1956; 11:676-677. Google Scholar
  2. Cheson BD, Bennett JM, Kopecky KJ. RevisedRevised recommendations of the International Working Group for diagnosis, standardization of response criteria, treatment outcomes, and reporting standards for therapeutic trials in acute myeloid leukemia. J Clin Oncol. 2003; 21(24):4642-4649. PubMedhttps://doi.org/10.1200/JCO.2003.04.036Google Scholar
  3. Döhner H, Estey EH, Amadori S. Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood. 2010; 115(3):453-474. PubMedhttps://doi.org/10.1182/blood-2009-07-235358Google Scholar
  4. Ferrara F, Schiffer CA. Acute myeloid leukaemia in adults. Lancet. 2013; 381(9865):484-495. PubMedhttps://doi.org/10.1016/S0140-6736(12)61727-9Google Scholar
  5. Döhner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia. N Engl J Med. 2015; 373(12):1136-1152. PubMedhttps://doi.org/10.1056/NEJMra1406184Google Scholar
  6. Harrison WJ. The total cellularity of the bone marrow in man. J Clin Pathol. 1962; 15:254-259. PubMedhttps://doi.org/10.1136/jcp.15.3.254Google Scholar
  7. Coustan-Smith E, Campana D. Should evaluation for minimal residual disease be routine in acute myeloid leukemia?. Curr Opin Hematol. 2013; 20(2):86-92. PubMedhttps://doi.org/10.1097/MOH.0b013e32835dd90aGoogle Scholar
  8. Hourigan CS, Karp JE. Minimal residual disease in acute myeloid leukaemia. Nat Rev Clin Oncol. 2013; 10(8):460-471. PubMedhttps://doi.org/10.1038/nrclinonc.2013.100Google Scholar
  9. Grimwade D, Freeman SD. Defining minimal residual disease in acute myeloid leukemia: which platforms are ready for “prime time”?. Blood. 2014; 124(23):3345-3355. PubMedhttps://doi.org/10.1182/blood-2014-05-577593Google Scholar
  10. Paietta E. Minimal residual disease in acute myeloid leukemia: coming of age. Hematology Am Soc Hematol Educ Program. 2012; 2012:35-42. PubMedhttps://doi.org/10.1182/asheducation-2012.1.35Google Scholar
  11. Hokland P, Ommen HB, Mulé MP, Hourigan CS. Advancing the minimal residual disease concept in acute myeloid leukemia. Semin Hematol. 2015; 52(3):184-192. PubMedhttps://doi.org/10.1053/j.seminhematol.2015.04.001Google Scholar
  12. Ommen HB. Monitoring minimal residual disease in acute myeloid leukaemia: a review of the current evolving strategies. Ther Adv Hematol. 2016; 7(1):3-16. https://doi.org/10.1177/2040620715614529Google Scholar
  13. Hayden JA, van der Windt DA, Cartwright JL, Cote P, Bombardier C. Assessing bias in studies of prognostic factors. Ann Intern Med. 2013; 158(4):280-286. PubMedhttps://doi.org/10.7326/0003-4819-158-4-201302190-00009Google Scholar
  14. Broglio KR, Quintana M, Foster M. Association of pathologic complete response to neoadjuvant therapy in HER2-positive breast cancer with long-term outcomes: a meta-analysis. JAMA Oncol. 2016; 2(6):751-760. Google Scholar
  15. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007; 8:16. PubMedhttps://doi.org/10.1186/1745-6215-8-16Google Scholar
  16. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002; 21(11):1539-1558. PubMedhttps://doi.org/10.1002/sim.1186Google Scholar
  17. Candoni A, Toffoletti E, Gallina R. Monitoring of minimal residual disease by quantitative WT1 gene expression following reduced intensity conditioning allogeneic stem cell transplantation in acute myeloid leukemia. Clin Transplant. 2011; 25(2):308-316. PubMedhttps://doi.org/10.1111/j.1399-0012.2010.01251.xGoogle Scholar
  18. Jacobsohn DA, Tse WT, Chaleff S. High WT1 gene expression before haematopoietic stem cell transplant in children with acute myeloid leukaemia predicts poor event-free survival. Br J Haematol. 2009; 146(6):669-674. PubMedhttps://doi.org/10.1111/j.1365-2141.2009.07770.xGoogle Scholar
  19. Rossi G, Carella AM, Minervini MM. Optimal time-points for minimal residual disease monitoring change on the basis of the method used in patients with acute myeloid leukemia who underwent allogeneic stem cell transplantation: a comparison between multiparameter flow cytometry and Wilms’ tumor 1 expression. Leuk Res. 2015; 39(2):138-143. PubMedhttps://doi.org/10.1016/j.leukres.2014.11.011Google Scholar
  20. Olszewski M, Chou PM, Huang W, Tallman S, Kletzel M. Correlation of minimal residual disease by assessing Wilms tumor gene expression and engraftment by variable number of tandem repeats in children with leukemia posthematopoietic stem cell transplantation. Pediatr Dev Pathol. 2006; 9(3):203-209. PubMedhttps://doi.org/10.2350/05-12-0004.1Google Scholar
  21. Kanakry CG, Tsai HL, Bolanos-Meade J. Single-agent GVHD prophylaxis with posttransplantation cyclophosphamide after myeloablative, HLA-matched BMT for AML, ALL, and MDS. Blood. 2014; 124(25):3817-3827. PubMedhttps://doi.org/10.1182/blood-2014-07-587477Google Scholar
  22. Anthias C, Dignan FL, Morilla R. Pre-transplant MRD predicts outcome following reduced-intensity and myeloablative allogeneic hemopoietic SCT in AML. Bone Marrow Transplant. 2014; 49(5):679-683. PubMedGoogle Scholar
  23. Buccisano F, Maurillo L, Spagnoli A. Cytogenetic and molecular diagnostic characterization combined to postconsolidation minimal residual disease assessment by flow cytometry improves risk stratification in adult acute myeloid leukemia. Blood. 2010; 116(13):2295-2303. PubMedhttps://doi.org/10.1182/blood-2009-12-258178Google Scholar
  24. Araki D, Wood BL, Othus M. Allogeneic hematopoietic cell transplantation for acute myeloid leukemia: time to move toward a minimal residual disease-based definition of complete remission?. J Clin Oncol. 2016; 34(4):329-336. PubMedhttps://doi.org/10.1200/JCO.2015.63.3826Google Scholar
  25. Zheng C, Zhu X, Tang B. The impact of pre-transplant minimal residual disease on outcome of intensified myeloablative cord blood transplant for acute myeloid leukemia in first or second complete remission. Leuk Lymphoma. 2016; 57(6):1398-1405. Google Scholar
  26. Bleyzac N, Cuzzubbo D, Renard C. Improved outcome of children transplanted for high-risk leukemia by using a new strategy of cyclosporine-based GVHD prophylaxis. Bone Marrow Transplant. 2016; 51(5):698-704. Google Scholar
  27. Ustun C, Courville EL, DeFor T. Myeloablative, but not reduced-intensity, conditioning overcomes the negative dffect of flow-cytometric evidence of leukemia in acute myeloid leukemia. Biol Blood Marrow Transplant. 2016; 22(4):669-675. PubMedhttps://doi.org/10.1016/j.bbmt.2015.10.024Google Scholar
  28. Tian H, Chen GH, Xu Y. Impact of pre-transplant disease burden on the outcome of allogeneic hematopoietic stem cell trans plant in refractory and relapsed acute myeloid leukemia: a single-center study. Leuk Lymphoma. 2015; 56(5):1353-1361. Google Scholar
  29. Walter RB, Gyurkocza B, Storer BE. Comparison of minimal residual disease as outcome predictor for AML patients in first complete remission undergoing myeloablative or nonmyeloablative allogeneic hematopoietic cell transplantation. Leukemia. 2015; 29(1):137-144. PubMedhttps://doi.org/10.1038/leu.2014.173Google Scholar
  30. Woehlecke C, Wittig S, Arndt C, Gruhn B. Prognostic impact of WT1 expression prior to hematopoietic stem cell transplantation in children with malignant hematological diseases. J Cancer Res Clin Oncol. 2015; 141(3):523-529. Google Scholar
  31. Goswami M, McGowan KS, Lu K. A multigene array for measurable residual disease detection in AML patients undergoing SCT. Bone Marrow Transplant. 2015; 50(5):642-651. PubMedhttps://doi.org/10.1038/bmt.2014.326Google Scholar
  32. Valkova V, Polak J, Markova M. Minimal residual disease detectable by quantitative assessment of WT1 gene before allogeneic stem cell transplantation in patients in first remission of acute myeloid leukemia has an impact on their future prognosis. Clin Transplant. 2013; 27(1):E21-29. PubMedGoogle Scholar
  33. Bastos-Oreiro M, Perez-Corral A, Martinez-Laperche C. Prognostic impact of minimal residual disease analysis by flow cytometry in patients with acute myeloid leukemia before and after allogeneic hemopoietic stem cell transplantation. Eur J Haematol. 2014; 93(3):239-246. PubMedhttps://doi.org/10.1111/ejh.12336Google Scholar
  34. Wang Y, Liu DH, Liu KY. Impact of pre-transplantation risk factors on post transplantation outcome of patients with acute myeloid leukemia in remission after haploidentical hematopoietic stem cell transplantation. Biol Blood Marrow Transplantat. 2013; 19(2):283-290. Google Scholar
  35. Grubovikj RM, Alavi A, Koppel A, Territo M, Schiller GJ. Minimal residual disease as a predictive factor for relapse after allogeneic hematopoietic stem cell transplant in adult patients with acute myeloid leukemia in first and second complete remission. Cancers (Basel). 2012; 4(2):601-617. Google Scholar
  36. Leung W, Pui CH, Coustan-Smith E. Detectable minimal residual disease before hematopoietic cell transplantation is prognostic but does not preclude cure for children with very-high-risk leukemia. Blood. 2012; 120(2):468-472. PubMedhttps://doi.org/10.1182/blood-2012-02-409813Google Scholar
  37. Laane E, Derolf AR, Bjorklund E. The effect of allogeneic stem cell transplantation on outcome in younger acute myeloid leukemia patients with minimal residual disease detected by flow cytometry at the end of post-remission chemotherapy. Haematologica. 2006; 91(6):833-836. PubMedGoogle Scholar
  38. van Dongen JJ, van der Velden VH, Bruggemann M, Orfao A. Minimal residual disease diagnostics in acute lymphoblastic leukemia: need for sensitive, fast, and standardized technologies. Blood. 2015; 125(26):3996-4009. PubMedhttps://doi.org/10.1182/blood-2015-03-580027Google Scholar
  39. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003; 327(7414):557-560. PubMedhttps://doi.org/10.1136/bmj.327.7414.557Google Scholar

Article Information

Vol. 102 No. 5 (2017): May, 2017 : Articles
 
DOI
https://doi.org/10.3324/haematol.2016.159343
Pubmed
28126965
Pubmed Central
PMC5477605
 
Published
2017-05-01
Published By
Ferrata Storti Foundation, Pavia, Italy
Print ISSN
0390-6078
Online ISSN
1592-8721
 

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