The CCAAT/enhancer binding proteins (CEBPs) have been involved in the etiology of acute leukemia (AL) and investigated in numerous genetic association studies, however, the results were inconclusive. The current meta-analysis was conducted to clarify the effect of CEBPE rs2239633 variant on childhood AL risk. Electronic literature search was performed on August 15, 2014, from databases of Medline, PubMed, Embase, and Web of Science. A total of 22 case-control studies were eligible for the pooled analysis. The results demonstrated that rs2239633 A allele was significantly associated with a decreased risk of childhood AL (A vs G: OR=0.87, 95%CI = 0.80, 0.94, p<0.001), especially in B-cell ALL subgroup (A vs G: OR = 0.79, 95%CI = 0.74, 0.83, p<0.001), but not among T-cell ALL or AML subgroups. In the stratified analysis by ethnicity, the association was observed in Europeans (A vs G: OR = 0.80, 95%CI = 0.76, 0.84, p<0.001) but not in Asian and mixed populations. Moreover, the results of sensitivity and cumulative meta-analysis indicated the robustness of our results. Also, Begg’s and Egger’s tests did not indicate any evidence of obvious asymmetry. In summary, our study provided evidence that CEBPE rs2239633 variant is associated with decreased risk of childhood B-cell ALL in Europeans.
Citation: Sun J, Zheng J, Tang L, Healy J, Sinnett D, Dai Y-e (2015) Association between CEBPE Variant and Childhood Acute Leukemia Risk: Evidence from a Meta-Analysis of 22 Studies. PLoS ONE 10(5): e0125657. https://doi.org/10.1371/journal.pone.0125657
Academic Editor: Ken Mills, Queen's University Belfast, UNITED KINGDOM
Received: December 2, 2014; Accepted: March 16, 2015; Published: May 4, 2015
Copyright: © 2015 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The study was supported by Science and Technology Development Foundation of Nanjing Medical University (No. 2014NLMU144). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Acute leukemia (AL), characterized by dysregulated clonal expansion of immature lymphoid or myeloid progenitor cells, is the most common cancer in children. In the United States, it accounts for~20 000 cancer diagnoses and 10 000 deaths.AL can be subdivided into acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL) according to the cell type. Studies for leukemogenesis have been conducted for many years, and previous studies provided evidence that infections and immunologic response might play a role in the etiology of childhood leukemia.[2,3] However, the mechanisms underlying the development of most AL remain unclear.[2,4]
The CCAAT/enhancer binding proteins (CEBPs) are transcription factors involved in hematopoietic cell development, including granulopoiesis. Akagiet al  showed that CEBPB and CEBPE double-knockout mice were highly susceptible to fatal infections and died within 2–3 months. Also, the proportion of hematopoietic progenitor cells in the bone marrow of the knockout mice was significantly increased. Recently, genome wide association (GWA) studies have identified candidate single nucleotide polymorphism (SNP) located in CEBPE (14q11.2), which was strongly related to the susceptibility of childhood ALL.[6,7] Taken together, these results suggested that CEBPE might appear to be a good candidate gene for childhood AL. Up to date, an accumulating number of studies focused on the association between CEBPE variant and ALL risk, however, the conclusions of these studies were inconsistent. Thus, we conducted a meta-analysis with an overall larger sample size by summarizing previous case-control studies to clarify the associations of polymorphisms in the CEBPE gene with susceptibility to childhood AL, including B-cell ALL, T-cell ALL and AML.
A comprehensive literature search of the Medline, Pubmed, Embase, and Web of Sciencerepositories was carried out using the following keywords: (‘acute leukemia’, ‘ALL’ or ‘acute lymphoblastic leukemia’ ‘acute myeloid leukemia’, ‘AML’), (‘polymorphism’, ‘variant’, ‘mutation’) and (‘CEBPE’, or ‘rs2239633’) (the last search update was August 15 2014). Moreover, the reference lists of review articles and retrieved articles were hand-searched for additional potential studies.
Inclusion and Exclusion Criteria
Studies meeting the following criteria were included in themeta-analysis: (1) reporting the association between CEBPE rs2239633 variant and childhood AL risk (2)providing sufficient data to estimate odds ratios (ORs) with 95% confidence intervals (95% CIs). A study was excluded if: (1) had no control population (2) investigated the adult acute leukemia.
The following information was gathered for each eligible study by two independent authors: name of the first author, year of publication, number of patients and healthy controls, sex and mean age in patients and healthy controls, country of origin, ethnicity of the individuals involved, method of genotyping, types of acute leukemia (e.g., B-cell ALL, T-cell ALL or AML), allele frequency and genotype frequency. The corresponding author would be contacted when the article did not provide sufficient genotype distributions. Moreover, disagreements were resolved by discussion between the two investigators.
Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated to assess the association between CEBPE rs2239633 polymorphism and risk of childhood acute leukemia. The significance of the pooled OR was determined by the Z-test, and a P value of less than 0.05 was considered significant. In the control group, the Hardy-Weinberg equilibrium (HWE) was assessed, and a P<0.05 was considered as significant disequilibrium. In addition, subgroup analysis was carried out stratified by types of AL and ethnicity. Between-study heterogeneity was evaluated using the χ2-based Q test and I2 test.[8,9] A P<0.10 or I2>50% was considered significant heterogeneity, and a random effects model (DerSimonian and Laird method) was used, otherwise a fixed effects model was applied. Subgroup analysis, meta-regression analysis and Galbraith plot were carried out to assess the potential source of heterogeneity.[11,12]
Sensitivity analysis by omitting one study at a time was also performed to assess the influence of individual studies on the combined risk estimate. We also carried out a cumulative meta-analysis to measure the genetic effect changes as evidence accumulating over time and measured the trend in estimated risk effect. Potential publication bias was assessed by visual inspection of the Begg’s funnel plot and statistically via Egger’s regression tests.[14,15] If publication bias was detected, we adjusted for the effect by means of Duval and Tweedie’s nonparametric trim-and-fill method. All statistical analyses were performed using the STATA software, version12 (StataCorp LP, College Station, Texas).
Main Characteristics of the Included Studies
Eighty-four references were identified during our premature searches, of which, 63 non-relevant articles were excluded following review of title and abstract. In the remaining 21 full text papers, 10 studies did not investigate the association between CEBPE rs2239633 polymorphism and childhood AL risk. The selection process is illustrated in Fig 1. Of these eligible articles, 3 publications reported data on different subpopulations,[17–19] 4 investigated on multiple disease types of childhood AL and we treated these studies independently.[7,17,20–22] Thus, a total of 22 studies involving 6152 patients and 11739 healthy controls met our selection criteria. (Table 1)
The summary of meta-analysis for the CEBPE polymorphism with acute leukemia involving 6152 patients and 11739 healthy controls is shown in Table 2. The results of combined analyses revealed a significant association of rs2239633 variant with acute leukemia risk at the allelic level (A vs G: OR = 0.87, 95%CI = 0.80, 0.94, p<0.001) (Fig 2) and AA vs GG: OR = 0.78, 95%CI = 0.67, 0.93, p = 0.005; at the genotype level under a recessive model (AA vs AG+GG: OR = 0.80, 95%CI = 0.73, 0.88, p<0.001). Moreover, in the stratified analysis by types of acute leukemia and ethnicity, lower risk was detected in the B-cell ALL subgroup (A vs G: OR = 0.79, 95%CI = 0.74, 0.83, p<0.001), ALL subgroup (A vs G: OR = 0.84, 95%CI = 0.79, 0.90, p<0.001) and European subgroup (A vs G: OR = 0.80, 95%CI = 0.76, 0.84, p<0.001). However, in the subgroups of T-cell, AML, Asians, and mixed populations, the results showed no evidence supporting associations between rs2239633 and disease risk. Moreover, a significant association was found in population-based subgroup (A vs G: OR = 0.78, 95%CI = 0.74, 0.83, p<0.001).
The sizes of the squares reflect the weighting of included studies. AL: acute leukemia; OR: odds ratio; CI: confidence interval.
Test of Heterogeneity
Significant between-study heterogeneity was detected in all genetic models expect for the recessive model. Further, we performed meta-regression to assess the potential source of heterogeneity by ethnicity, disease types, sample size, and year of publication. The results showed that disease types, source of control, ethnicity and year of publication were the potential sources of heterogeneity, which explained 79% (p = 0.001), 52.8% (p = 0.01), 57.1% (p = 0.006), and 31.2% (p = 0.03) of τ2, respectively. Moreover, Galbraith plot analysis indicated studies as outliers, which were possible sources of heterogeneity, when excluded, the heterogeneity was non-significant (Ph = 0.77 for A vs G) and the association was still significant (A vs G: OR = 0.83, 95%CI = 0.79, 0.88, p<0.001). (Fig 3)
Sensitivity Analyses and Cumulative Meta-Analysis
In the sensitivity analysis, the pooled OR did not qualitatively change by omitting a single study at a time. (Fig 4) In addition, the results of cumulative meta-analysis showed that the pooled ORs of rs2239633 variant tended to be stable and the associations tended toward significant associations with accumulation of more data over time, indicating the robustness of our results. (Fig 5)
Results were computed by omitting each study (left column) in turn. Bars: 95% confidence interval. AL: acute leukemia.
Publication bias was assessed by Begg’s and Egger’s tests. The shapes of the funnel plots did not indicate any evidence of obvious asymmetry. (Fig 6) Moreover, the results showed no evidence of publication bias in the allelic association test. (AA vs GG: P = 0.18 for Begg’s test, P = 0.12 for Egger’s test)
Recent studies showed that CEBPE, along with other 4 CEBP family members, have been targeted by recurrent immunoglobulin heavy chain translocations in B-cell precursor ALL, suggesting a possible role of CEBPE insusceptibility to ALL. Moreover, the relationship between CEBPE rs2239633polymorphism and childhood ALL has been investigated in several genetic association studies, though the results of these studies were contradictory and inconclusive, owing to the small sample sizes and ethnic difference.[17,21,24,25] Meta-analysis is a statistical method, which can overcome the problem of small sample sizes and inadequate statistical power in different studies.[26,27]
In the present meta-analysis, we found a significant association between CEBPE variant and childhood ALL risk in the overall population. In the subgroup analysis by ethnicity, the results indicated that CEBPE rs2239633 polymorphism was significantly associated with childhood AL in Europeans, but not among mixed and Asian populations, suggesting that the relative contribution of individual susceptibility genes may vary across different populations. Previous studies demonstrated that racial and ethnic disparities persist in the development of ALL. In general, Hispanic whites have the highest incidence of ALL, while blacks are less likely to develop nearly all AL subtypes. Additionally, compared with white and Asian children, black children had the lowest overall survival and event-free survival rates. When the data were stratified by AL subtypes, the significant correlation was only detected in the B-cell ALL subgroup, similar result was observed in a recent meta-analysis.  However, the recent meta-analysis did not investigate the association of CEBPE variant with susceptibility to T-cell ALL or AML. Here, in the subgroup analysis, significant association was not observed in T-cell ALL (A vs G: OR = 0.86, 95%CI = 0.67, 1.09, p = 0.23) and AML (A vs G: OR = 1.19, 95%CI = 0.73, 1.96, p = 0.48) subgroups. Childhood AL is a group of diseases with varied immunophenotypes and genetic changes. It is suspected that B-cell or T-cell ALL and AML may not share a common etiology. In the US, ALL is diagnosed in approximately 2000 children each year, whereas AML is diagnosed in only about 500 children. Moreover, the cure rate of children with ALL was approximately 90%,[33,34] while the survival in patients with AML was only 60% to 70% in developed countries.[35–37] These findings suggested that CEBPE polymorphism was only associated with susceptibility to B-cell ALL subtype, which was also supported by the evidence that GSTM1 and XRCC1 Arg399Glnvariants were only associated with ALL but not AML.[38,39] However, in our study, only 4 and 2 studies were eligible for the analysis of T-cell ALL and AML, respectively, which might compromisethe reliability of these findings. Thus, further studies with larger sample size are required to validate the possible role of CEBPE polymorphisms in childhood AL, especially in T-cell ALL and AML.
In our meta-analysis, heterogeneity was detected in most genetic models. Thus, we carried out meta-regression and Galbraith plot analyses to assess the sources contributing to the heterogeneity. The results of meta-regression showed that types of disease, source of control, ethnicity and year of publication could explain 79%, 52.8%, 57.1%, and 31.2% of τ2, respectively, indicating that these factors were the potential sources of between-study heterogeneity. In addition, the Galbraith plot demonstrated five studies that were the potential origin of heterogeneitysince, when excluded, the heterogeneity was removed. In these studies, disease subtype heterogeneity [17,40] and ethnicity differences  might explain some heterogeneity.
This meta-analysis increased statistical power by pooling data from all eligible studies, whereas several limitations should be acknowledged in our meta-analysis. First, we only included studies published in English, which might introduce a language bias. Second, sample size was small for some subgroups, such as T-cell ALL and AML subgroups, which might limit the precision of the pooled estimates, suggesting that more large sample sizes, precise and stratified studies (especially about childhood T-cell ALL and AML are still in urgent need for further evaluation. Moreover, publication bias occurred because we only selected published articles to acquire data for analyses. Also, significant heterogeneity was detected in major comparisons, while disease types and ethnicity were identified as the potential sources of heterogeneity by meta-regression and Galbraith plot analyses. Finally gene-gene and gene–environment interactions were not analyzed due to insufficient data.
Despite these limitations, our results are significant. Our meta-analysis demonstrates that CEBPE rs2239633may be associated with susceptibilitytoB-cell ALL in the European population. However, there is lack of evidence showing the correlation between this polymorphism and risk of T-cell ALL and AML, which needs to be validated by further well-designed genetic association studies with larger sample sizes. Moreover, gene–gene and gene–environment interactions should also be investigated in future studies.
Conceived and designed the experiments: YD JS. Performed the experiments: LT JZ. Analyzed the data: YD LT JZ. Contributed reagents/materials/analysis tools: YD LT JH DS JS JZ. Wrote the paper: YD JH DS JS.
- 1. Pui CH. Childhood leukemias. The New England journal of medicine. 1995;332(24):1618–30. Epub 1995/06/15. pmid:7753142.
- 2. Schmiegelow K, Vestergaard T, Nielsen SM, Hjalgrim H. Etiology of common childhood acute lymphoblastic leukemia: the adrenal hypothesis. Leukemia. 2008;22(12):2137–41. Epub 2008/08/23. pmid:18719616.
- 3. Wiemels J. Perspectives on the causes of childhood leukemia. Chemico-biological interactions. 2012;196(3):59–67. Epub 2012/02/14. pmid:22326931; PubMed Central PMCID: PMC3839796.
- 4. Armstrong SA, Look AT. Molecular genetics of acute lymphoblastic leukemia. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2005;23(26):6306–15. Epub 2005/09/13. pmid:16155013.
- 5. Akagi T, Thoennissen NH, George A, Crooks G, Song JH, Okamoto R, et al. In vivo deficiency of both C/EBPbeta and C/EBPepsilon results in highly defective myeloid differentiation and lack of cytokine response. PloS one. 2010;5(11):e15419. Epub 2010/11/13. pmid:21072215; PubMed Central PMCID: PMC2972224.
- 6. Xu H, Yang W, Perez-Andreu V, Devidas M, Fan Y, Cheng C, et al. Novel susceptibility variants at 10p12.31–12.2 for childhood acute lymphoblastic leukemia in ethnically diverse populations. Journal of the National Cancer Institute. 2013;105(10):733–42. Epub 2013/03/21. pmid:23512250; PubMed Central PMCID: PMC3691938.
- 7. Papaemmanuil E, Hosking FJ, Vijayakrishnan J, Price A, Olver B, Sheridan E, et al. Loci on 7p12.2, 10q21.2 and 14q11.2 are associated with risk of childhood acute lymphoblastic leukemia. Nature genetics. 2009;41(9):1006–10. Epub 2009/08/18. pmid:19684604.
- 8. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in medicine. 2002;21(11):1539–58. Epub 2002/07/12. pmid:12111919.
- 9. Zintzaras E, Lau J. Synthesis of genetic association studies for pertinent gene-disease associations requires appropriate methodological and statistical approaches. Journal of clinical epidemiology. 2008;61(7):634–45. Epub 2008/06/10. pmid:18538260.
- 10. DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled clinical trials. 1986;7(3):177–88. Epub 1986/09/01. pmid:3802833.
- 11. Galbraith RF. A note on graphical presentation of estimated odds ratios from several clinical trials. Statistics in medicine. 1988;7(8):889–94. Epub 1988/08/01. pmid:3413368.
- 12. Lu XC, Yu W, Tao Y, Zhao PL, Li K, Tang LJ, et al. Contribution of transforming growth factor alpha polymorphisms to nonsyndromic orofacial clefts: a HuGE review and meta-analysis. American journal of epidemiology. 2014;179(3):267–81. Epub 2013/11/19. pmid:24243742.
- 13. Copas J, Shi JQ. Meta-analysis, funnel plots and sensitivity analysis. Biostatistics. 2000;1(3):247–62. Epub 2003/08/23. pmid:12933507.
- 14. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34. Epub 1997/10/06. pmid:9310563; PubMed Central PMCID: PMC2127453.
- 15. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088–101. Epub 1994/12/01. pmid:7786990.
- 16. Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–63. Epub 2000/07/06. pmid:10877304.
- 17. Emerenciano M, Barbosa TC, Lopes BA, Blunck CB, Faro A, Andrade C, et al. ARID5B polymorphism confers an increased risk to acquire specific MLL rearrangements in early childhood leukemia. BMC cancer. 2014;14:127. Epub 2014/02/26. pmid:24564228; PubMed Central PMCID: PMC3948138.
- 18. Ellinghaus E, Stanulla M, Richter G, Ellinghaus D, te Kronnie G, Cario G, et al. Identification of germline susceptibility loci in ETV6-RUNX1-rearranged childhood acute lymphoblastic leukemia. Leukemia. 2012;26(5):902–9. Epub 2011/11/15. pmid:22076464; PubMed Central PMCID: PMC3356560.
- 19. Prasad RB, Hosking FJ, Vijayakrishnan J, Papaemmanuil E, Koehler R, Greaves M, et al. Verification of the susceptibility loci on 7p12.2, 10q21.2, and 14q11.2 in precursor B-cell acute lymphoblastic leukemia of childhood. Blood. 2010;115(9):1765–7. Epub 2010/01/01. pmid:20042726.
- 20. Ross JA, Linabery AM, Blommer CN, Langer EK, Spector LG, Hilden JM, et al. Genetic variants modify susceptibility to leukemia in infants: a Children's Oncology Group report. Pediatric blood & cancer. 2013;60(1):31–4. Epub 2012/03/17. pmid:22422485; PubMed Central PMCID: PMC3381932.
- 21. Vijayakrishnan J, Sherborne AL, Sawangpanich R, Hongeng S, Houlston RS, Pakakasama S. Variation at 7p12.2 and 10q21.2 influences childhood acute lymphoblastic leukemia risk in the Thai population and may contribute to racial differences in leukemia incidence. Leukemia & lymphoma. 2010;51(10):1870–4. Epub 2010/10/06. pmid:20919861.
- 22. Orsi L, Rudant J, Bonaventure A, Goujon-Bellec S, Corda E, Evans TJ, et al. Genetic polymorphisms and childhood acute lymphoblastic leukemia: GWAS of the ESCALE study (SFCE). Leukemia. 2012;26(12):2561–4. Epub 2012/06/05. pmid:22660188.
- 23. Akasaka T, Balasas T, Russell LJ, Sugimoto KJ, Majid A, Walewska R, et al. Five members of the CEBP transcription factor family are targeted by recurrent IGH translocations in B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Blood. 2007;109(8):3451–61. Epub 2006/12/16. pmid:17170124.
- 24. Healy J, Richer C, Bourgey M, Kritikou EA, Sinnett D. Replication analysis confirms the association of ARID5B with childhood B-cell acute lymphoblastic leukemia. Haematologica. 2010;95(9):1608–11. Epub 2010/05/13. pmid:20460642; PubMed Central PMCID: PMC2930966.
- 25. Pastorczak A, Gorniak P, Sherborne A, Hosking F, Trelinska J, Lejman M, et al. Role of 657del5 NBN mutation and 7p12.2 (IKZF1), 9p21 (CDKN2A), 10q21.2 (ARID5B) and 14q11.2 (CEBPE) variation and risk of childhood ALL in the Polish population. Leukemia research. 2011;35(11):1534–6. Epub 2011/09/06. pmid:21889209.
- 26. Li S, Ren L, Fan L, Wang G. IKZF1 rs4132601 polymorphism and acute lymphoblastic leukemia susceptibility: a meta-analysis. Leukemia & lymphoma. 2014:1–13. Epub 2014/07/12. pmid:25012940.
- 27. He J, Wang F, Zhu JH, Chen W, Cui Z, Jia WH. No association between MTR rs1805087 A>G polymorphism and non-Hodgkin lymphoma susceptibility: Evidence from 11486 subjects. Leukemia & lymphoma. 2014:1–23. Epub 2014/06/24. pmid:24956146.
- 28. Lim JY, Bhatia S, Robison LL, Yang JJ. Genomics of racial and ethnic disparities in childhood acute lymphoblastic leukemia. Cancer. 2014;120(7):955–62. Epub 2014/01/03. pmid:24382716; PubMed Central PMCID: PMC4015138.
- 29. Dores GM, Devesa SS, Curtis RE, Linet MS, Morton LM. Acute leukemia incidence and patient survival among children and adults in the United States, 2001–2007. Blood. 2012;119(1):34–43. Epub 2011/11/17. pmid:22086414; PubMed Central PMCID: PMC3251235.
- 30. Bhatia S, Sather HN, Heerema NA, Trigg ME, Gaynon PS, Robison LL. Racial and ethnic differences in survival of children with acute lymphoblastic leukemia. Blood. 2002;100(6):1957–64. Epub 2002/08/30. pmid:12200352.
- 31. Wang C, Chen J, Sun H, Sun L, Liu Y. CEBPE polymorphism confers an increased risk of childhood acute lymphoblastic leukemia: a meta-analysis of 11 case-control studies with 5,639 cases and 10,036 controls. Annals of hematology. 2015;94(2):181–5. Epub 2014/09/10. pmid:25195121.
- 32. Greaves M. Infection, immune responses and the aetiology of childhood leukaemia. Nature reviews Cancer. 2006;6(3):193–203. Epub 2006/02/10. pmid:16467884.
- 33. Inaba H, Greaves M, Mullighan CG. Acute lymphoblastic leukaemia. Lancet. 2013;381(9881):1943–55. Epub 2013/03/26. pmid:23523389; PubMed Central PMCID: PMC3816716.
- 34. Hunger SP, Lu X, Devidas M, Camitta BM, Gaynon PS, Winick NJ, et al. Improved survival for children and adolescents with acute lymphoblastic leukemia between 1990 and 2005: a report from the children's oncology group. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2012;30(14):1663–9. Epub 2012/03/14. pmid:22412151; PubMed Central PMCID: PMC3383113.
- 35. Rubnitz JE. How I treat pediatric acute myeloid leukemia. Blood. 2012;119(25):5980–8. Epub 2012/05/09. pmid:22566607; PubMed Central PMCID: PMC3383013.
- 36. Gibson BE, Webb DK, Howman AJ, De Graaf SS, Harrison CJ, Wheatley K. Results of a randomized trial in children with Acute Myeloid Leukaemia: medical research council AML12 trial. British journal of haematology. 2011;155(3):366–76. Epub 2011/09/10. pmid:21902686.
- 37. Rubnitz JE, Inaba H, Dahl G, Ribeiro RC, Bowman WP, Taub J, et al. Minimal residual disease-directed therapy for childhood acute myeloid leukaemia: results of the AML02 multicentre trial. The Lancet Oncology. 2010;11(6):543–52. Epub 2010/05/11. pmid:20451454; PubMed Central PMCID: PMC3171799.
- 38. Tang Q, Li J, Zhang S, Yuan B, Sun H, Wu D, et al. GSTM1 and GSTT1 null polymorphisms and childhood acute leukemia risk: evidence from 26 case-control studies. PloS one. 2013;8(10):e78810. Epub 2013/11/07. pmid:24194954; PubMed Central PMCID: PMC3806859.
- 39. Qin L, Chen X, Li P, Yang Z, Mo W. Comprehensive assessment of the association between DNA repair gene XRCC3 Thr241Met polymorphism and leukemia risk. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine. 2014;35(3):2521–8. Epub 2013/11/08. pmid:24197983.
- 40. Wang Y, Chen J, Li J, Deng J, Rui Y, Lu Q, et al. Association of three polymorphisms in ARID5B, IKZF1 and CEBPE with the risk of childhood acute lymphoblastic leukemia in a Chinese population. Gene. 2013;524(2):203–7. Epub 2013/04/24. pmid:23608171.