|
Advertisement |
|
|
||
 |
|
|||||
|
|||||
|
 |
||||||
|
||||||
|
||||||
|
Originally published as JCO Early Release 10.1200/JCO.2005.03.6137 on November 7 2005 © 2005 American Society of Clinical Oncology. Overexpression of the ETS-Related Gene, ERG, Predicts a Worse Outcome in Acute Myeloid Leukemia With Normal Karyotype: A Cancer and Leukemia Group B StudyFrom the Division of Hematology and Oncology, Department of Internal Medicine, and Division of Human Cancer Genetics, Department of Microbiology, Virology, Immunology, and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH; The Cancer and Leukemia Group B Statistical Center; Duke University Medical Center, Durham; Wake Forest University School of Medicine, Winston-Salem, NC; North Shore University Hospital, Manhasset; Roswell Park Cancer Institute, Buffalo, NY; University of Chicago, Chicago, IL; and University of Alabama at Birmingham, Birmingham, AL. C.D. Baldus is currently at Charité, Campus Benjamin Franklin, Medizinische Klinik III, Berlin, Germany. Address reprint requests to Guido Marcucci, MD, The Ohio State University, The Comprehensive Cancer Center, A433B Starling-Loving Hall, 320 W 10th Ave, Columbus OH 43210; e-mail: marcucci-1{at}medctr.osu.edu
PURPOSE: To test the prognostic significance of ETS-related gene (ERG) expression in cytogenetically normal primary acute myeloid leukemia (AML). PATIENTS AND METHODS: Pretreatment blood samples from 84 cytogenetically normal AML patients aged less than 60 years, who were characterized for BAALC expression, FLT3 internal tandem duplication (ITD), and MLL partial tandem duplication (PTD) and uniformly treated on Cancer and Leukemia Group B 9621 protocol, were analyzed for ERG expression by real-time reverse transcriptase polymerase chain reaction. Patients were divided into quartiles according to ERG levels and were compared for clinical outcome. High-density oligonucleotide arrays were used to identify genes differentially expressed between high and low ERG expressers. RESULTS: With a median follow-up of 5.7 years, patients with the upper 25% of ERG expression values had a worse cumulative incidence of relapse (CIR; P < .001) and overall survival (OS; P = .011) than the remaining patients. In a multivariable analysis, high ERG expression (P < .001) and the presence of MLL PTD (P = .027) predicted worse CIR. With regard to OS, an interaction was observed between expression of ERG and BAALC (P = .013), with ERG overexpression predicting shorter survival only in low BAALC expressers (P = .002). ERG overexpression was an independent prognostic factor even when the unfavorable group of FLT3 ITD patients lacking an FLT3 wild-type allele was included. High ERG expression was associated with upregulation of 112 expressed-sequenced tags and named genes, many of which are involved in cell proliferation, differentiation, and apoptosis. CONCLUSION: ERG overexpression in AML patients with normal cytogenetics predicts an adverse clinical outcome and seems to be associated with a specific molecular signature.
Cytogenetic abnormalities detected at diagnosis have long been recognized as predictors for clinical outcome in acute myeloid leukemia (AML).1 However, the largest cytogenetic subset of adult AML, approximately 45%, consists of patients with a normal karyotype.1 In large studies of the clinical significance of cytogenetics in AML, these patients have been categorized in an intermediate-risk group, with 5-year survival rates varying between 24% and 42%.2-5 The difference in clinical outcome likely reflects molecular heterogeneity of this cytogenetic subset whose prognosis is influenced by submicroscopic gene mutations or overexpression.6 The adverse prognostic impact of the partial tandem duplication (PTD) of MLL, internal tandem duplication (ITD) of FLT3, and overexpression of BAALC in karyotypically normal AML is now established,7-17 as is the favorable prognostic significance of CEBPA gene mutations.17,18 However, it is likely that, in addition to the aforementioned genetic abnormalities, others will be found to impact on the clinical outcome of cytogenetically normal AML. Given that intensive treatments such as allogeneic stem-cell transplantation (SCT), although potentially curative in patients with poor prognosis AML, are associated with high treatment-related mortality, novel molecular markers will likely be valuable to stratify karyotypically normal AML patients to risk-adapted therapies. Furthermore, because these markers are mutated or overexpressed genes encoding proteins with potentially pivotal roles in leukemogenesis, they could also serve as molecular targets for novel therapeutic approaches.19 We have recently shown that ETS-related gene (ERG), which is located at chromosome band 21q22, is frequently overexpressed in AML patients with complex karyotypes and cryptic amplification of chromosome 21.20 ERG and other members of the ETS family are downstream effectors of mitogenic signaling transduction pathways and are involved in key steps regulating cell proliferation, differentiation, and apoptosis.21-23 Although ERG rearrangements have been found in AML24 and Ewing sarcoma25 and its overexpression has been observed in prostate cancer,26 little is known regarding how ERG contributes to malignant transformation.27 In our previous report,20 high ERG expression was not always associated with genomic amplification, thereby leaving ERG overexpression mechanistically unexplained. Nevertheless, the recurrent presence of ERG overexpression in AML with complex karyotypes, a prognostically unfavorable subgroup, suggests that ERG overexpression might not only be a nonrandom event in myeloid leukemogenesis, but also might contribute to an aggressive malignant phenotype. To test this hypothesis, we analyzed karyotypically normal AML patients who were uniformly treated on the Cancer and Leukemia Group B (CALGB) 9621 protocol.28 We show that the level of ERG expression varies among patients and that ERG overexpression constitutes an adverse prognostic factor in cytogenetically normal AML.
Patients ERG expression was analyzed in 84 adults aged less than 60 years with primary, untreated AML and normal cytogenetics confirmed by central morphologic and karyotype reviews. Eligible patients were enrolled onto the treatment trial CALGB 9621,28 the prospective cytogenetic study CALGB 8461,29 and the molecular study of BAALC expression (CALGB 9665).15 Written institutional review board–approved informed consent was obtained from all patients. Pretreatment cytogenetic analyses of bone marrow (BM) were performed as previously described.4,30At least 20 metaphases were analyzed, and the karyotype was normal in each case. Pretreatment MLL PTD and FLT3 ITD status and BAALC levels were also determined centrally for each patient, as described previously.7,10,15
Treatment
RNA Extraction and Real-Time Reverse Transcriptase Polymerase Chain Reaction
The comparative cycle threshold (CT) method was used to determine the relative expression levels of ERG to GPI, the internal control, in patients previously included in the BAALC expression study.15 Of the original 86 patients, 84 were analyzed for ERG/GPI levels calculated using the mean of To assess the impact of ERG levels on clinical outcome, we also adopted an alternative approach. Absolute ERG copy numbers were measured and normalized to the copy numbers of ABL, which was a different internal control validated by multicenter studies,31,32 using standard curves constructed as reported previously.33 We analyzed 73 samples comprising 48 samples from patients included in the set evaluated for ERG/GPI levels and for whom leftover material was available and 25 samples from additional, karyotypically normal AML patients enrolled onto CALGB 9621. Importantly, eight of the latter patients had the unfavorable FLT3ITD/– genotype,10 the presence of which was a reason for exclusion from the previous BAALC study.15 Positive and negative controls were included in all assays. The reproducibility of the real-time RT-PCR assays was similar to what we reported previously.15
Gene Expression Profiling
Definition of Clinical End Points Cumulative incidence of relapse (CIR) was measured from the CR date to date of relapse, death, or date last known alive, where death in CR was considered a competing risk. Disease-free survival (DFS) was measured from the CR date until date of relapse or death (regardless of cause), censoring for patients alive at last follow-up. DFS was used only for the 73 patients analyzed for ERG/ABL because none of them died in CR, and therefore, DFS reflected the actual relapse risk. Overall survival (OS) was measured from the date the patient was enrolled onto the study until the date of death or date last known alive.
Statistical Methods
Estimated probabilities of OS and DFS were calculated using the Kaplan-Meier method, and the log-rank test evaluated differences between survival distributions. Estimates of CIR were calculated, and Gray's k-sample test35 evaluated differences in relapse rates. Proportional hazards models were constructed for OS and DFS,36 whereas a multivariable model using Gray's method was constructed for CIR37 using a limited backwards selection procedure. Variables remaining in the final models were significant at
For microarray data analysis, normalization and model-based expression index (MBEI) computations were performed using dChip version 1.3 (Harvard University, Cambridge, MA).38-40 Only the perfect match probes were used in the computation of MBEIs, whereas mismatch probes were ignored. Log[MBEI] values were calculated and then exported to BRB-ArrayTools v3.2.3 (National Cancer Institute, Bethesda, MD) for further analysis. Probe sets with a variance in log[MBEI] values above the 80th percentile were retained for further analyses (n = 10,935). A comparison of gene expression between Q1-3 and Q4 was performed by two-sample t tests using
Impact of ERG Expression on Clinical Outcome No significant differences were observed in most pretreatment clinical characteristics between patients with the lowest 75% (Q1-3) and highest 25% (Q4) of ERG expression values. The two groups differed for BAALC expression levels (P = .042), circulating (P = .045) and BM (P = .056) blast percentages, gum hypertrophy (P = .060), and French-American-British subgroup distribution (P = .023; Table 1).
The CR rate was 81%, with no significant difference between patients in Q4 and Q1-3 (P = .532; Table 2). With a median follow-up of 5.7 years (range, 4.4 to 7.4 years), patients in Q4 had a worse CIR than patients in Q1-3 (P < .001; Fig 1A). The estimated 5-year relapse rate for Q4 patients was 81% compared with 33% for Q1-3 patients (Table 2). Furthermore, the OS was different between the two groups (P = .011; Fig 1B). Patients in Q4 had a median survival time of 1.2 years and an estimated 5-year survival rate of 19%; in contrast, median survival time for Q1-3 patients has not been reached, and their estimated 5-year survival rate was 51% (Table 2). When analysis was restricted to the set of patients who achieved CR and received APBSCT as a prescribed consolidation treatment (n = 49), ERG remained a significant adverse factor for outcome (data not shown).
On multivariable analysis, high ERG expression (ie, Q4) adversely impacted CIR (P < .001), as did the presence of MLL PTD (P = .027; Table 3, Fig 2A). Patients in Q4 had an estimated relapse risk almost four times higher than patients in Q1-3. For OS, an interaction between expression of ERG and BAALC (P = .013) was observed (Table 3). For low BAALC levels, patients in Q4 had a shorter survival than patients in Q1-3 (P = .002; Fig 2B, Table 3). However, the adverse impact of high ERG expression on OS was not observed in patients with high BAALC expression (P = .922; Fig 2C, Table 3). The only patients who maintained a long-term survival rate greater than 50% were patients with lower expression of both ERG and BAALC. Additionally, worse survival was associated with higher log[WBC] (P = .012; Table 3). Although Q4 patients had a higher percentage of circulating blasts at diagnosis (Table 1), this factor did not impact significantly on clinical outcome and, consequently, was not included in the multivariable models.
Similar results were obtained when we used a second analytic strategy uing specific standard curves to measure ERG copy numbers normalized to ABL.32,33 To explore whether ERG expression predicts clinical outcome independently from other major unfavorable prognostic markers, we included samples with the most unfavorable FLT3 genotype, FLT3ITD/–. Normalized ERG copies ranged from 0.4 to 735.9 (median, 17.9 copies). Patients with the lowest 75% (Q1-3; n = 55) of ERG copies (median, 11.0 copies; range, 0.4 to 35.1 copies) were compared with patients with the highest 25% (Q4; n = 18) of ERG copies (median, 62.2 copies; range, 37.5 to 735.9 copies). There were no significant differences in FLT3 genotype distribution, which included wild-type FLT3 (FLT3WT/WT), FLT3 ITD expressing the wild-type allele (FLT3ITD/WT), and FLT3 ITD lacking the wild-type allele (FLT3ITD/–; P = .644). Patients in Q4 had a worse DFS (P = .092) and OS (P = .003) than patients in Q1-3, with an estimated 5-year DFS of 25% (95% CI, 6% to 50%) v 53% (95% CI, 38% to 66%), respectively, and OS of 22% (95% CI, 7% to 43%) v 54% (95% CI, 40% to 67%), respectively. When the analysis was restricted to the subset of 25 patients not analyzed for ERG/GPI, ERG overexpression remained a significant adverse predictor for both OS and DFS (data not shown). In a multivariable model, high ERG copy number (ie, Q4; P = .023; Fig 3A) and FLT3 ITD (P < .001) independently predicted worse DFS. The estimated relapse risk for Q4 patients was more than twice the risk for Q1-3 patients (hazard ratio [HR] = 2.55; 95% CI, 1.14 to 5.69). Patients with FLT3ITD/– and FLT3ITD/WT had an estimated relapse risk eight times (HR = 8.37; 95% CI, 2.91 to 24.08) and three times (HR = 3.23; 95% CI, 1.44 to 7.23) the risk of patients with FLT3WT/WT, respectively. In a multivariable model for OS, high ERG (HR = 3.05; 95% CI, 1.53 to 6.05; P = .002; Fig 3B), high log[WBC] (HR = 1.68; 95% CI, 1.14 to 2.46; P = .009), and FLT3 ITD (P = .002) predicted shorter survival. Patients with FLT3ITD/– and FLT3ITD/WT had six times (HR = 6.21; 95% CI, 2.42 to 15.93) and two times (HR = 2.09; 95% CI, 0.98 to 4.47), respectively, the estimated risk of dying compared with FLT3WT/WT patients.
Gene Expression Profiling by Oligonucleotide Microarrays in High Versus Low ERG Microarray gene expression profiling was conducted to assess whether ERG overexpression was associated with a specific signature suggestive of the gene's potential leukemogenic role. One hundred seventeen probes in the Affymetrix U133 plus 2.0 GeneChip were differentially expressed (P < .001) between the Q1-3 and Q4 groups, including 63 unique, named genes and 49 expressed-sequenced tags. Twenty-five probes corresponding to 14 named genes (Table 4) and eight expressed-sequenced tags had at least a two-fold difference in expression levels between the Q1-3 and Q4 groups (Fig 4). Supporting our real-time RT-PCR results, three probe sets for ERG were differentially expressed, with average expression 2.1 to 2.6 times higher in Q4 patients.
Of the 63 named genes overexpressed in Q4, functional characterization was available for 53. Twenty-three of the genes are involved in DNA and/or RNA binding and chromatin remodeling, including general transcription activators (ie, GTF2H2, FAM48A, TCF12, and GCN5L2) or repressors (CTBP2) and lineage-specific transcription regulators, such as BCL11A, which is involved in lymphoid cell development,41 and HEMGN, which is involved in myeloid differentiation.42-44 Other genes encode small GTPases, members of the RAS superfamily (RALA and RAB10) or their regulators (RABGAP1 and ARHGAP22), or proteins involved in membrane-receptor signaling pathways (GPR21, DPAGT1, PILRB, GPR89, and FRMD4B), including activators of small GTPases. Surprisingly, we found overexpression of three proapoptotic genes (PACAP, IKIP, and DAPK1); DAPK1 was recently reported to be silenced by methylation in AML.45 Finally, the following three genes with unknown function but linked to hereditary diseases were upregulated: ATXN2, which is mutated in autosomal-dominant spinocerebellar ataxia-2,46 SHANK3, which is involved in the 22q13 deletion syndrome,47 and OFD1, which is mutated in orofaciodigital syndrome I.48 To our knowledge, overexpression or mutations of these genes have hitherto not been reported in AML.
Although molecular mechanisms underlying diverse clinical outcome in cytogenetically normal AML are not fully understood, recent studies have identified several biomarkers correlated with prognosis.6-18 Here, we report for the first time that ERG overexpression predicted an increased relapse risk and short survival in AML patients with normal karyotype by both univariable and multivariable analyses. Despite a relatively small number of patients studied, our data support the addition of ERG overexpression to the emerging list of markers predictive for clinical outcome in cytogenetically normal AML. With the number of prognostic markers growing, the relative contribution of each in predicting treatment outcome becomes important. In the current study, although high ERG levels correlated with an increased relapse risk regardless of the BAALC expression, a prognostic interaction between ERG and BAALC expression was observed for OS, with a shorter survival associated with high ERG levels only among patients with low BAALC expression. Likewise, interactions among prognostic markers, such as a greater prognostic importance of FLT3 ITD compared with BAALC overexpression and of BAALC overexpression compared with CEBPA mutations, have been reported previously.15,17,18 Because data on the impact of the concurrent presence of two or more molecular markers in cytogenetically normal AML are limited, further investigation of prognostic interactions is required by large prospective studies, with the goal of designing a prioritized, clinically relevant prognostic classification. Although our data suggest that expression of ERG is useful for prognostic stratification of cytogenetically normal AML, the mechanism through which ERG overexpression contributes to myeloid leukemogenesis remains unknown. ERG is one of more than 30 members of the ETS gene family, most of which are downstream nuclear targets of signal transduction pathways regulating and promoting cell differentiation, proliferation, and tissue invasion.21,22,27 Rearrangement of ERG was initially discovered in Ewing sarcomas harboring t(21;22)(q22;q12), which at the molecular level fuses ERG with EWS.25 Other ETS family members also fuse with EWS in Ewing sarcomas.49-51 In AML carrying t(16;21)(p11;q22), ERG was found rearranged with FUS, linking ERG with myeloid leukemogenesis for the first time.24 Interestingly, like EWS, FUS is a member of the TET family of RNA-binding proteins,52 supporting the notion that gene rearrangements involving ETS members are often characterized by a TET-related transactivation domain at the N terminus and ETS DNA binding and protein-protein interaction domains at the C terminus. This structure likely increases the oncogenic activity of the resulting chimeric transcription factors by redirecting them to specific targets. Interestingly, a role for ERG in endothelial cell differentiation and angiogenesis was recently suggested.23,53 Our microarray gene expression profiling analysis, using a supervised analysis, identified a molecular signature for patients in the highest Q (Q4) of ERG expression. Many genes upregulated in Q4, including ERG, encode proteins functioning as transcription factors or involved in chromatin remodeling and RNA processing; others encode proteins regulating cell differentiation, proliferation, and apoptosis. Interestingly, the recently described HEMGN gene (also known as EDAG) was the most differentially expressed gene between the two groups. This gene reportedly regulates proliferation, differentiation, and apoptosis of hematopoietic cells and seems to be significantly overexpressed in refractory AML patients but not in patients with chemotherapy-sensitive disease.42-44 Because the molecular signature for the ERG overexpressers was defined by patients' clustering based on previously identified prognostic groups (ie, Q4 v Q1-3), it was not surprising that this gene profile was associated with a worse clinical outcome (data not shown). Obviously, for ERG expression to become a molecular marker used routinely for risk stratification in AML with normal cytogenetics, our data require confirmation in an independent, large patient cohort.
The Appendix is included in the full-text version of this article, available online at www.jco.org. It is not included in the PDF (via Adobe® Acrobat Reader®) version.
The authors indicated no potential conflicts of interest.
We thank Marko I. Klisovic for his excellent technical support.
Supported by National Cancer Institute (Bethesda, MD) Grants No. CA101140, CA77658, CA102031, CA31946, CA09512, CA16058, and CA90469 and The Coleman Leukemia Research Foundation. Both G.M. and C.D. Baldus contributed equally to this work. Authors' disclosures of potential conflicts of interest are found at the end of this article.
1. Mrózek K, Heerema NA, Bloomfield CD: Cytogenetics in acute leukemia. Blood Rev 18:115-136, 2004[CrossRef][Medline] 2. Grimwade D, Walker H, Oliver F, et al: The importance of diagnostic cytogenetics on outcome in AML: Analysis of 1,612 patients entered into the MRC AML 10 trial. Blood 92:2322-2333, 1998 3. Slovak ML, Kopecky KJ, Cassileth PA, et al: Karyotypic analysis predicts outcome of preremission and postremission therapy in adult acute myeloid leukemia: A Southwest Oncology Group/Eastern Cooperative Oncology Group study. Blood 96:4075-4083, 2000 4. Byrd JC, Mrózek K, Dodge RK, et al: Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: Results from Cancer and Leukemia Group B (CALGB 8461). Blood 100:4325-4336, 2002 5. Farag SS, Ruppert AS, Mrózek K, et al: Outcome of induction and postremission therapy in younger adults with acute myeloid leukemia with normal karyotype: A Cancer and Leukemia Group B study. J Clin Oncol 23:482-493, 2005 6. Marcucci G, Mrózek K, Bloomfield CD: Molecular heterogeneity and prognostic biomarkers in adults with acute myeloid leukemia and normal cytogenetics. Curr Opin Hematol 12:68-75, 2005[CrossRef][Medline] 7. Caligiuri MA, Strout MP, Lawrence D, et al: Rearrangement of ALL1 (MLL) in acute myeloid leukemia with normal cytogenetics. Cancer Res 58:55-59, 1998 8. Schnittger S, Kinkelin U, Schoch C, et al: Screening for MLL tandem duplication in 387 unselected patients with AML identify a prognostically unfavorable subset of AML. Leukemia 14:796-804, 2000[CrossRef][Medline] 9. Döhner K, Tobis K, Ulrich R, et al: Prognostic significance of partial tandem duplications of the MLL gene in adult patients 16 to 60 years old with acute myeloid leukemia and normal cytogenetics: A study of the Acute Myeloid Leukemia Study Group Ulm. J Clin Oncol 20:3254-3261, 2002 10. Whitman SP, Archer KJ, Feng L, et al: Absence of the wild-type allele predicts poor prognosis in adult de novo acute myeloid leukemia with normal cytogenetics and the internal tandem duplication of FLT3: A Cancer and Leukemia Group B study. Cancer Res 61:7233-7239, 2001 11. Kottaridis PD, Gale RE, Frew ME, et al: The presence of a FLT3 internal tandem duplication in patients with acute myeloid leukemia (AML) adds important prognostic information to cytogenetic risk group and response to the first cycle of chemotherapy: Analysis of 854 patients from the United Kingdom Medical Research Council AML 10 and 12 trials. Blood 98:1752-1759, 2001 12. Thiede C, Steudel C, Mohr B, et al: Analysis of FLT3-activating mutations in 979 patients with acute myelogenous leukemia: Association with FAB subtypes and identification of subgroups with poor prognosis. Blood 99:4326-4335, 2002 13. Schnittger S, Schoch C, Dugas M, et al: Analysis of FLT3 length mutations in 1003 patients with acute myeloid leukemia: Correlation to cytogenetics, FAB subtype, and prognosis in the AMLCG study and usefulness as a marker for the detection of minimal residual disease. Blood 100:59-66, 2002 14. Fröhling S, Schlenk RF, Breitruck J, et al: Prognostic significance of activating FLT3 mutations in younger adults (16 to 60 years) with acute myeloid leukemia and normal cytogenetics: A study of the AML Study Group Ulm. Blood 100:4372-4380, 2002 15. Baldus CD, Tanner SM, Ruppert AS, et al: BAALC expression predicts clinical outcome of de novo acute myeloid leukemia patients with normal cytogenetics: A Cancer and Leukemia Group B study. Blood 102:1613-1618, 2003 16. Baldus CD, Thiede C, Bloomfield CD, et al: High BAALC expression as an independent adverse risk factor in 309 patients with acute myeloid leukemia (AML) and normal cytogenetics: Results of the SHG'96 study. Blood 104:554a, 2004 (abstr 2009) 17. Bienz M, Ludwig M, Oppliger Leibundgut E, et al: Risk assessment in patients with acute myeloid leukemia and a normal karyotype. Clin Cancer Res 11:1416-1424, 2005 18. Fröhling S, Schlenk RF, Stolze I, et al: CEBPA mutations in younger adults with acute myeloid leukemia and normal cytogenetics: Prognostic relevance and analysis of cooperating mutations. J Clin Oncol 22:624-633, 2004 19. Whitman SP, Liu S, Vukosavljevic T, et al: The MLL partial tandem duplication: Evidence for recessive gain-of-function in acute myeloid leukemia identifies a novel patient subgroup for molecular-targeted therapy. Blood 106:345-352, 2005 20. Baldus CD, Liyanarachchi S, Mrózek K, et al: Acute myeloid leukemia with complex karyotypes and abnormal chromosome 21: Amplification discloses overexpression of APP, ETS2, and ERG genes. Proc Natl Acad Sci U S A 101:3915-3920, 2004 21. Oikawa T: ETS transcription factors: Possible targets for cancer therapy. Cancer Sci 95:626-633, 2004[CrossRef][Medline] 22. Oikawa T, Yamada T: Molecular biology of the Ets family of transcription factors. Gene 303:11-34, 2003[CrossRef][Medline] 23. McLaughlin F, Ludbrook VJ, Cox J, et al: Combined genomic and antisense analysis reveals that the transcription factor Erg is implicated in endothelial cell differentiation. Blood 98:3332-3339, 2001 24. Ichikawa H, Shimizu K, Hayashi Y, et al: An RNA-binding protein gene, TLS/FUS, is fused to ERG in human myeloid leukemia with t(16;21) chromosomal translocation. Cancer Res 54:2865-2868, 1994 25. Sorensen PHB, Lessnick SL, Lopez-Terrada D, et al: A second Ewing's sarcoma translocation, t(21;22), fuses the EWS gene to another ETS-family transcription factor, ERG. Nat Genet 6:146-151, 1994[CrossRef][Medline] 26. Petrovics G, Liu A, Shaheduzzaman S, et al: Frequent overexpression of ETS-related gene-1 (ERG1) in prostate cancer transcriptome. Oncogene 24:3847-3852, 2005[CrossRef][Medline] 27. Hart AH, Corrick CM, Tymms MJ, et al: Human ERG is a proto-oncogene with mitogenic and transforming activity. Oncogene 10:1423-1430, 1995[Medline] 28. Kolitz JE, George SL, Dodge RK, et al: Dose escalation studies of cytarabine, daunorubicin, and etoposide with and without multidrug resistance modulation with PSC- 833 in untreated adults with acute myeloid leukemia younger than 60 years: Final induction results of Cancer and Leukemia Group B Study 9621. J Clin Oncol 22:4290-4301, 2004 29. Döhner H, Arthur DC, Ball ED, et al: Trisomy 13: A new recurring chromosome abnormality in acute leukemia. Blood 76:1614-1621, 1990 30. Mitelman F (ed): ISCN (1995): An International System for Human Cytogenetic Nomenclature. Basel, Switzerland, Karger, 1995 31. Beillard E, Pallisgaard N, van der Velden VHJ, et al: Evaluation of candidate control genes for diagnosis and residual disease detection in leukemic patients using ‘real-time’ quantitative reverse-transcriptase polymerase chain reaction (RQ-PCR): A Europe Against Cancer program. Leukemia 17:2474-2486, 2003[CrossRef][Medline] 32. Gabert J, Beillard E, van der Velden VHJ, et al: Standardization and quality control studies of ‘real-time’ quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia: A Europe Against Cancer program. Leukemia 17:2318-2357, 2003[CrossRef][Medline] 33. Marcucci G, Caligiuri MA, Döhner H, et al: Quantification of CBFbeta/MYH11 fusion transcript by real-time RT-PCR in patients with INV(16) acute myeloid leukemia. Leukemia 15:1072-1080, 2001[CrossRef][Medline] 34. Cheson BD, Cassileth PA, Head DR, et al: Report of the National Cancer Institute-sponsored workshop on definitions of diagnosis and response in acute myeloid leukemia. J Clin Oncol 8:813-819, 1990[Abstract] 35. Gray RJ: A class of k-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat 16:1141-1154, 1988[CrossRef] 36. Cox DR: Regression models and life tables. J R Stat Soc B 34:187-202, 1972 37. Fine JP, Gray RJ: A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94:496-509, 1999[CrossRef] 38. Li C, Wong W: DNA-chip analyzer (dChip), in Parmigiani G, Garrett ES, Irizarry R, et al (eds): The Analysis of Gene Expression Data: Methods and Software. New York, NY, Springer-Verlag, 2003, pp 120-141 39. Schadt EE, Li C, Ellis B, et al: Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J Cell Biochem 37:120-125, 2001 (suppl)[CrossRef] 40. Li C, Wong WH: Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proc Natl Acad Sci U S A 98:31-36, 2001 41. Satterwhite E, Sonoki T, Willis TG, et al: The BCL11 gene family: Involvement of BCL11A in lymphoid malignancies. Blood 98:3413-3420, 2001 42. Li CY, Zhan YQ, Xu CW, et al: EDAG regulates the proliferation and differentiation of hematopoietic cells and resists cell apoptosis through the activation of nuclear factor-kappaB. Cell Death Differ 11:1299-1308, 2004[CrossRef][Medline] 43. Yang LV, Nicholson RH, Kaplan J, et al: Hemogen is a novel nuclear factor specifically expressed in mouse hematopoietic development and its human homologue EDAG maps to chromosome 9q22, a region containing breakpoints of hematological neoplasms. Mech Dev 104:105-111, 2001[CrossRef][Medline] 44. An L-L, Li G, Wu K-F, et al: High expression of EDAG and its significance in AML. Leukemia 19:1499-1502, 2005[CrossRef][Medline] 45. Voso MT, Scardocci A, Guidi F, et al: Aberrant methylation of DAP-kinase in therapy-related acute myeloid leukemia and myelodysplastic syndromes. Blood 103:698-700, 2004 46. Furtado S, Payami H, Lockhart PJ, et al: Profile of families with parkinsonism-predominant spinocerebellar ataxia type 2 (SCA2). Mov Disord 19:622-629, 2004[CrossRef][Medline] 47. Wilson HL, Wong ACC, Shaw SR, et al: Molecular characterisation of the 22q13 deletion syndrome supports the role of haploinsufficiency of SHANK3/PROSAP2 in the major neurological symptoms. J Med Genet 40:575-584, 2003 48. Romio L, Wright V, Price K, et al: OFD1, the gene mutated in oral-facial-digital syndrome type 1, is expressed in the metanephros and in human embryonic renal mesenchymal cells. J Am Soc Nephrol 14:680-689, 2003 49. Delattre O, Zucman J, Plougastel B, et al: Gene fusion with an ETS DNA-binding domain caused by chromosome translocation in human tumours. Nature 359:162-165, 1992[CrossRef][Medline] 50. Urano F, Umezawa A, Hong W, et al: A novel chimera gene between EWS and E1A-F, encoding the adenovirus E1A enhancer-binding protein, in extraosseous Ewing's sarcoma. Biochem Biophys Res Commun 219:608-612, 1996[CrossRef][Medline] 51. Jeon I-S, Davis JN, Braun BS, et al: A variant Ewing's sarcoma translocation (7;22) fuses the EWS gene to the ETS gene ETV1. Oncogene 10:1229-1234, 1995[Medline] 52. de Alava E, Pardo J: Ewing tumor: Tumor biology and clinical applications. Int J Surg Pathol 9:7-17, 2001[Medline] 53. Baltzinger M, Mager-Heckel A-M, Remy P: Xl erg: Expression pattern and overexpression during development plead for a role in endothelial cell differentiation. Dev Dyn 216:420-433, 1999[CrossRef][Medline] Submitted July 28, 2005; accepted September 19, 2005.
This article has been cited by other articles:
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
|||||||||||
|
|||||||||||
|
|
Copyright © 2005 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
|
TOP
ABSTRACT
INTRODUCTION
CT from two replicates and expressed as 2µ(
1,500/µL and platelets 
= .05. Adjusted survival curves were generated from the proportional hazards and Gray models using average covariate values. 
