Purpose: Aberrant methylation of promoter-associated CpG islands is an epigenetic oncogenic mechanism. The objective of this study was to define the methylation characteristics of patients with acute lymphocytic leukemia (ALL).

Experimental Design: Using bisulfite-PCR followed by restriction enzyme digestion (COBRA), we have analyzed the methylation status of 10 promoter-associated CpG islands in 80 untreated adult patients with ALL.

Results: Mean methylation density of MDR1, THBS2, MYF3, ER, p15, THBS1, CD10, C-ABL, and p16 was 24.5%, 20.8%, 17.6%, 16.1%, 11.3%, 8.9%, 4.5%, 3.7%, and 1.3% respectively. p73 was methylated in 17 of 80 cases (21.2%). A total of 86.2% of the cases had methylation of at least one gene, and 42.5% of the cases had methylation of three or more genes. MDR1 methylation was inversely correlated with age (P = 0.01). CD10 methylation inversely correlated with CD10 expression (P = 0.0001). Methylation of MDR1 and THBS1 was inversely associated with the presence of the Philadelphia chromosome, whereas C-ABL methylation correlated with the presence of the p210 variant of the Philadelphia chromosome. In univariate analysis, methylation of THBS1 was associated with a favorable outcome (P = 0.02), whereas methylation of p73, p15, and C-ABL was associated with a trend toward worse prognosis.

Conclusions: Aberrant DNA methylation of promoter-associated CpG islands is very common in adult ALL and potentially defines subgroups with distinct clinical and biological characteristics.

Aberrant methylation of promoter-associated CpG islands has emerged as a distinct molecular pathway leading to cellular malignant transformation. By silencing key regulatory genes, such as tumor suppressor genes (1, 2), DNA methylation is the epigenetic equivalent of mutation/deletion in oncogenesis. The causes of aberrant methylation are unknown. In particular, it is not clear whether methylation represents rare random events selected for during carcinogenesis or results from underlying factors, including defects in the methylation machinery. It has recently been proposed that human malignancies can be classified according to their methylation characteristics in two different phenotypes: (a) tumors characterized by the simultaneous methylation of multiple CpG islands; and (b) tumors in which this phenomenon is not observed (3). Of importance, differences in methylation phenotype are associated with distinct clinical and genetic characteristics (4), suggesting that aberrant methylation of multiple promoter-associated CpG islands is not a random process but the result of as yet unidentified molecular defects that promote increased methylation of multiple CpG islands. This phenomenon has also been observed in gastric cancer (5), pancreatic cancer (6), and AML3(3, 7, 8).

ALL includes a heterogeneous group of malignant lymphoid disorders with different biological and clinical features (9). Prognosis and therapy in ALL are based on age, immunophenotype, karyotype, and predicted risk of central nervous system involvement (9)(10). Several genes are known to be abnormally methylated in ALL (11), including calcitonin(12, 13), ER(14), p15(15, 16, 17), p16(18, 19), HIC-1(20), p73(21, 22), and E-cadherin(23), among others. No study has evaluated the characteristics of multiple promoter-associated CpG islands in ALL.

To study the methylation characteristics of patients with ALL and its possible clinical/biological associations, we have analyzed the methylation status of 10 genes in 80 untreated adult patients with ALL. Our results suggest that aberrant methylation of multiple promoter-associated CpG islands is a common event in ALL, suggesting that methylation profiling could be used to develop a molecular epigenetic classification of ALL.

Samples.

Stored frozen mononuclear cells from bone marrow from 80 untreated adult patients with ALL were used in this study. Patients were selected solely based on sample availability. All patients were treated with the hyper-CVAD chemotherapy program (24) at the University of Texas M. D. Anderson Cancer Center. Patient characteristics and results of this program have been reported elsewhere (24). Consent for sample drawing and storage was obtained from all patients following institutional guidelines.

Bisulfite Modification of DNA.

Methods for bisulfite modification of DNA and subsequent PCR techniques used in this study are described in detail on the World Wide Web.4 Bisulfite treatment of DNA converts unmethylated CpG sites to UpG without modifying methylated sites, thus allowing their differentiation by allele-specific PCR, restriction digestion, or sequencing. Bisulfite treatment of genomic DNA was performed as described previously (25). In summary, DNA was extracted from bone marrow mononuclear cells using standard phenol-chloroform methods. After extraction, 2 μg of DNA were used for bisulfite treatment. This quantity of bisulfite-treated DNA can be used to analyze at least 10 genes by PCR. DNA was denatured in 0.2 n NaOH at 37°C for 10 min and incubated with 3 m sodium bisulfite at 50°C for 16 h. DNA was then purified using the Wizard cleanup system (Promega) and desulfonated with 0.3 n NaOH at 25°C for 5 min. DNA was then precipitated with ammonium acetate and ethanol, washed with 70% ethanol, dried, and resuspended in H2O.

Primer Design.

To minimize overestimation of methylated alleles, the following points were considered: (a) primers were designed to contain a minimum number of CpG dinucleotides in their sequence to avoid biased amplification of methylated alleles. If primers contained CpG sites, they were designed to amplify methylated and unmethylated alleles equally (with a mixture of C or T used for the sense strand or a mixture of G or A used for antisense primers); (b) primers were designed to contain a maximum number of thymidines converted from cytosines to avoid amplification of the nonconverted genomic sequence; (c) amplification of genomic DNA not treated with bisulfite was always carried out to monitor lack of nonspecific amplification; (d) primers were designed to be within 300 bp of known transcription start sites; and (e) for each set of primers, we tested multiple restriction enzymes to confirm the methylation status and sequence of the fragment analyzed and performed mixing experiments (using methylated and unmethylated templates mixed at a known ratios) to exclude any potential amplification bias. Primer sequences, coordinates, GenBank accession numbers, the number of expected restriction fragments, and PCR conditions are shown in Table 1.

PCR and Restriction Digestion.

PCR reactions were carried in 50-μl reactions. In each reaction, 2 μl of bisulfite-treated DNA were used, as well as 1.25 mm deoxynucleotide triphosphate, 6.7 mm MgCl2, 5 μl of PCR buffer, 1 nmol of primers, and 1 unit of Taq polymerase. All PCR reactions were performed using a hot start at 95°C for 5 min. After amplification, PCR products (except for p73) were digested with specific restriction enzymes that digest alleles that were methylated before bisulfite treatment and separated in nondenaturing polyacrylamide gels. Gels were stained with ethidium bromide. The proportion of methylated versus unmethylated product (digested versus undigested) was quantitated by densitometric analysis, determining the density of methylation. Densitometric analysis was performed using a Bio-Rad Geldoc 2000 digital analyzer equipped with Quantity One version 4.0.3 software. Due to the small size of the reported sequence in the promoter region of p73, we used MSP (21) to analyze the methylation status of this gene. This technique only allows a qualitative analysis of methylation. A list of primer sequences and restriction enzymes used is shown in Table 1.

Statistical Analyses.

Correlation coefficients were computed between individual gene methylation values and the patient characteristics age, gender, percentage of blasts, WBC and platelet counts at presentation, albumin, cytogenetic abnormalities, immunophenotype and number of courses to achieve remission, and the risk of relapse as determined by a previously reported model (24). Extent of methylation for the 10 individual genes was compared between patients with and without the Ph chromosome, by means of the Wilcoxon rank-sum test. Agreement between two categorical variables was represented by a κ statistic. Correspondence of extent of methylation for the 10 genes considered was assessed by Spearman rank correlation coefficient. CR and DFS have been defined elsewhere (24). Estimates of 5-year DFS and OS rates were based on the Kaplan-Meier method, and differences were tested using the log-rank test. For some of the analyses below, for which a qualitative assessment of methylation was required, we used a threshold of ≥15% to define a sample as methylated. This threshold allows for factoring in methylation density when defining a sample as methylated and is similar to the method we used in previous studies. The use of a 10% threshold instead of a 15% threshold affects only 5% of the methylation measurements and does not appreciably change the results. All Ps reported are two-sided.

Patient Population.

We have analyzed the methylation characteristics of pretreatment bone marrow samples from 80 adult patients with ALL uniformly treated with the hyper-CVAD chemotherapy program at a single institution (24). The clinicopathological features of these patients are described in Table 2. The group studied here differed from the total hyper-CVAD cohort in several clinical features. These included a higher WBC count (P = 0.001) and lactate dehydrogenase level (P = 0.001) at presentation. These differences did not translate into prognostic differences in terms of CR rate (91% for both groups), DFS (32% versus 38% at 5 years), or OS (34% versus 39% at 5 years).

Methylation Status of 10 Selected Genes.

Genes studied included ER, p15, p16, MDR1, THBS1, THBS2, C-ABL, p73, MYF3, and CD10. Different criteria were used for gene selection. ER(14), p15(17), p16(19), p73(21), and MYF3(26) were selected because of their frequent methylation in ALL. MDR1(27), THBS1(28), and its homologue, THBS2,5 were studied because they have been found to be methylated in other malignancies, and their abnormal expression could have potentially important roles in ALL. We have also studied C-ABL, which is hypermethylated in Ph-positive chronic myelogenous leukemia (29, 30) and in Ph-positive ALL (31). CD10 (CALLA) has recently been shown to be methylated and silenced in prostate cancer and is frequently expressed in ALL (32). For all these genes, with the exception of p73, we have analyzed four to five normal bone marrow specimens, none of which showed significant (>2%) methylation (data not shown). p73 has been shown not to be methylated in normal bone marrows by other investigators (21, 22).

Fig. 1 shows representative examples of methylation of each gene. Table 3 summarizes the distribution of methylation densities for each individual gene. Methylation (>2%) was lowest for the p16 gene (6 of 80 cases) and highest for the THBS2 gene (74 of 80 cases). Because aberrant hypermethylation will likely not only influence the number of CpG islands methylated but also influence the density of methylation of each particular island, we have quantitated the median methylation density of cases considered methylated (≥15%). These ranged from 30.2% for THBS1 to 46.6% for C-ABL. To assess the reproducibility of the methylation assay used here, we repeated 150 of the 720 non-MSP reactions. The Spearman rank order correlation coefficient between different measurements was r = 0.811 (P = 0.000000). The methylation status of p73 was analyzed using MSP (21); therefore, no quantitative analysis could be performed. Seventeen (21%) cases were methylated at p73.

Simultaneous Methylation of Multiple CpG Islands in ALL.

Using a methylation density cutoff point of 15% and considering MSP-positive reactions as indicative of significant methylation, 11 patients (13.75%) had methylation of 0 genes, 17 patients (21.25%) had methylation of 1 or 2 genes, 8 patients (10%) had methylation of 3 genes, 11 patients (13.75%) had methylation of 4 genes, 8 patients (10%) had methylation of 5 genes, 5 patients (6.25%) had methylation of 6 genes, and 1 patient (1.25%) had methylation of 7 or 8 genes. No case was found to have methylation of more than 8 genes (Fig. 2). By analyzing the methylation status of 10 genes simultaneously, we were able to study correlations between their methylation status. Positive correlations among pairs of individual genes are summarized in Table 4. Methylation of ER was associated with methylation of MDR1, THBS2, and MYF3 (all r values = 0.51), as well as THBS1 and p73 (r = 0.36 and κ = 0.23, respectively). CD10 was weakly associated with THBS1 and THBS2 but not with ER, MDR, MYF3, or p73. By contrast, methylation of p15 and C-ABL showed no significant correlation with each other or with any of the above-mentioned group of genes. p16 was only methylated (≥15%) in two cases, precluding correlation analyses.

Clinical and Pathological Correlates.

Using univariate analyses, we have explored possible correlations between methylation of a particular locus/loci with clinical characteristics and known prognostic factors in ALL. Factors analyzed included age, gender, percentage of blasts, WBC and platelet counts at presentation, albumin, risk model (24), Ph chromosome and other cytogenetic abnormalities, immunophenotype, and courses to achieve CR. An inverse association was found between MDR1 methylation and age (r = −0.25), but no associations with gender, blasts, WBC and platelet count at presentation, albumin, or risk model were detected for any individual gene or cluster of genes. Immunophenotype was associated with several distinct abnormalities. First, CD10 methylation was almost completely restricted to CD10-negative cases (CD10 expression <20% by flow cytometry): only one of eight cases with CD10 methylation was considered CD10 positive (CD10 expression = 54%); the rest (seven of eight) were CD10 negative with a range of CD10 expression of 0–4% (r = −0.43; P < 0.0001). THBS1 methylation was less frequent in CALLA-positive cases compared with those with B- or T-cell phenotype (P < 0.01). No association was found between T-cell immunophenotype and methylation of any of the genes studied, including p15.

We next studied associations between chromosomal changes and aberrant methylation in ALL. C-ABL methylation (≥15%) was associated with the presence of the Ph chromosome: C-ABL was methylated in 5 of 19 Ph-positive cases versus 1 of 61 Ph-negative cases (P = 0.01). The only Ph-negative case with methylation of C-ABL had multiple chromosomal abnormalities. p210BCR/ABL and p190BCR/ABL status was known for 14 of the 19 Ph-positive cases. None of the 10 p190BCR/ABL cases was methylated at C-ABL, whereas all 4 p210BCR/ABL cases were methylated at c-abl (density of methylation, 28–80%). Differences were also observed between Ph-positive and Ph-negative cases for THBS1 (0 of 19 Ph-positive cases methylated versus 16 of 61 Ph-negative cases, P = 0.01) and MDR1 (2 of 19 Ph-positive cases methylated versus 34 of 61 Ph-negative cases, P = 0.005). p15 methylation was associated with a diploid karyotype. No other associations could be found with other chromosomal abnormalities. Table 5 summarizes associations between clinical/biological characteristics and methylation.

Next we studied the impact of methylation of each individual gene in CR rate, DFS, and OS. THBS1 methylation was associated with a better OS (66% versus 28% at 5 years; P = 0.02) and DFS (37% versus 29% at 5 years; P = 0.04). Methylation of MDR1 was associated with a trend toward better OS (47% versus 22% at 5 years; P = 0.09). In contrast, methylation of p73 and p15 were associated with a poorer prognosis (Table 6). As expected, c-abl methylation was associated with a worse outcome: OS was 36% versus 0% at 5 years (P = 0.26). These results were reproduced when only the patients with Ph-negative disease were evaluated (Table 6).

The aim of this study was to determine the methylation characteristics of patients with adult ALL and explore possible associations between aberrant methylation and clinical/biological characteristics. This study is the first to analyze the methylation status of multiple promoter-associated CpG islands from a large cohort of uniformly treated adult patients with ALL. Results indicate that methylation of multiple genes is a common phenomenon in ALL; 87% of cases had at least 1 gene methylated, whereas 42.5% of cases had 3 or more genes methylated. In addition, methylation of several of the genes analyzed here was strongly correlated, suggesting that a molecular defect inducing hypermethylation of multiple CpG islands may occur in a subgroup of patients with ALL.

We have found several important clinical/biological associations with gene-specific methylation. CD10 methylation is inversely associated with CD10 expression, indicating that methylation of the CD10 promoter is associated with its silencing, as has been reported in prostate cancer (33). We have also found a striking association between methylation of C-ABL and the presence of the Ph chromosome. C-ABL methylation was detected only in those cases with the p210BCR/ABL variant as opposed to the p190BCR/ABL form, as has been reported by Shteper et al.(31). Other important findings include the inverse association between MDR and THBS1 methylation and the presence of the Ph. In particular, the inverse association between MDR1 methylation and the Ph abnormality suggests that MDR1-mediated chemotherapy resistance mechanisms may be involved in the poor prognosis of patients with Ph-positive acute leukemias. An inverse association between MDR1 methylation and age was also observed. No other relevant clinical/biological features were found to be associated with abnormal methylation patterns. In particular, we could not find any of the previously described (17) associations between p15 methylation and cytogenetic abnormalities, despite the larger number of patients analyzed in this study. This may be related to the specific patient populations studied or to the fact that we used a different technique to study p15 methylation than other studies. In preliminary univariate analysis, we found correlates between methylation and outcome, including methylation of THBS1, MDR1, and ER associated with a favorable outcome, and methylation of p15, p73, and c-abl associated with a worse outcome. Such correlates have been described previously for ER methylation in AML (34). These studies need to be confirmed and extended in a larger group, where multivariate analysis will be possible.

This study has several limitations. Our results are partially limited by the number of genes studied. By increasing the number of genes profiled, one could refine the concept of the hypermethylator phenotype and expand our knowledge about the clinical and biological implications of the hypermethylator phenotype in ALL. The development of microarray technologies to study methylation in the near future will help clarify these issues (35). In turn, the number of cases studied limits the clinical implications of our results. Given the heterogeneity of patients with ALL, a larger study of uniformly treated patients might allow multivariate analyses that are needed to address these issues. Finally, aberrant methylation of promoter-associated CpG islands is characterized by transcriptional inactivation and subsequent loss of function of the gene regulated in this fashion without structural modifications. In the present study, we have not directly analyzed the silencing of the genes studied. However, evidence reported by other investigators, including us, demonstrates that methylation of all genes studied here is usually associated with transcriptional silencing (14, 16, 21, 27, 28, 32). The strong inverse association found between CD10 methylation and expression serves as a surrogate marker that methylation, at least of CD10, is indeed associated with gene silencing, as studied here. Finally, in view of the number of genes and variables analyzed in this study, these results should be considered as hypothesis-generating and need to be confirmed in future studies.

The study has several important implications. Methylation profiling in ALL could provide useful markers complementing standard immunophenotypic and cytogenetic studies. It may also provide clues related to mechanisms of disease resistance, in particular those related to the presence of the Ph. By studying methylation patterns during remission and at relapse, methylation profiling could be developed to detect minimal residual disease. In addition, demethylating agents, such as 5-aza-2′-deoxycytidine, have shown promising activity in myelodysplasia (36) and AML (37). Hypomethylation of target genes such as p15 has been shown to correlate with clinical responses in patients with chronic myelogenous leukemia (38). In view of the extent of aberrant methylation demonstrated by our analysis, clinical trials using hypomethylating agents are indicated in patients with relapsed ALL. Prospective knowledge of pretreatment methylation patterns may help determine candidate patients for these therapies.

In summary, our results demonstrate that aberrant methylation in ALL is a common phenomenon in ALL. This knowledge may have implications in developing a molecular classification of ALL and may help develop new markers of minimal residual disease and determine candidate patients for demethylating therapies.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1

Supported in part by a Translational Research Grant from the Leukemia and Lymphoma Society of America and Research Grant RPG-99-098-01 from the American Cancer Society to J-P. J. I. Samples were obtained from a core bank at University of Texas M. D. Anderson Cancer Center funded by Contract PO1CA49639 from the NIH to S. M. K. G. G-M. is the recipient of Career Development Award from the American Society of Clinical Oncology.

3

The abbreviations used are: AML, acute myelocytic leukemia; ALL, acute lymphocytic leukemia; ER, estrogen receptor; Ph, Philadelphia chromosome; MSP, methylation-specific PCR; CR, complete remission; DFS, disease-free survival; OS, overall survival; CALLA, common acute lymphocytic leukemia antigen.

4

http://www.mdanderson.org/leukemia/methylation.

5

X-Q. Chen, L. L. Shen, N. Ahuja, G. Garcia-Manero, and J-P. Issa. THBS2 promoter methylation and silencing in cancer, submitted for publication.

Fig. 1.

Examples of methylation analysis of multiple CpG islands in ALL. A, aberrant methylation was detected by bisulfite-PCR followed by restriction enzyme digestion. Unmethylated (top rows) and methylated (bottom rows, arrows) bands were quantitated by densitometry. Methylation density (percentage of restricted versus unrestricted fragment) is shown above each lane. B, for analysis of aberrant methylation of p73, MSP was used. Two different PCR reactions were carried out, one with primers specific for the methylated sequence (Meth), and one with primers for the unmethylated sequence (Unmeth). This technique does not allow quantitative analysis. A sample was considered methylated if a band was visualized in the methylated reaction. Appropriate controls (C) consisted of cell lines with known methylation of the gene studied.

Fig. 1.

Examples of methylation analysis of multiple CpG islands in ALL. A, aberrant methylation was detected by bisulfite-PCR followed by restriction enzyme digestion. Unmethylated (top rows) and methylated (bottom rows, arrows) bands were quantitated by densitometry. Methylation density (percentage of restricted versus unrestricted fragment) is shown above each lane. B, for analysis of aberrant methylation of p73, MSP was used. Two different PCR reactions were carried out, one with primers specific for the methylated sequence (Meth), and one with primers for the unmethylated sequence (Unmeth). This technique does not allow quantitative analysis. A sample was considered methylated if a band was visualized in the methylated reaction. Appropriate controls (C) consisted of cell lines with known methylation of the gene studied.

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Fig. 2.

Methylation profile of the cohort studied. We have analyzed the methylation status of 10 genes in 80 patients with ALL. A sample was considered methylated for a particular gene if the density of methylation was ≥15%, or if a band was present in the methylated reaction for p73 (black boxes). White boxes indicate nonmethylated cases.

Fig. 2.

Methylation profile of the cohort studied. We have analyzed the methylation status of 10 genes in 80 patients with ALL. A sample was considered methylated for a particular gene if the density of methylation was ≥15%, or if a band was present in the methylated reaction for p73 (black boxes). White boxes indicate nonmethylated cases.

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Table 1

Genes studied

Gene/accesion no.Primers (coordinates)aRestriction enzyme/restriction sitesAnnealing temperatures (no. of cycles)
ER GGTTTTTGAGTTTTTTGTTTTG(300) BstUI 60 (3), 57 (4), 54 (5), and 51 (25) 
X03635 AACTTACTACTATCCAAATACACCTC(505)  
p15 GGAGTTTAAGGGGGTGGG (24747) BstUI 59 (37) 
AC000049 CCTAAATTACTTCTAAAAAAAAAC (24918)  
p16 GGTTTTGGYGAGGGTTGTTT (917) Taq58 (3), 56 (4), 54(5), and 52 (23) 
X94154 ACCCTATCCCTCAAATCCTCTAAAA (1078)  
MDR1 GTTATAGGAAGTTTGAGTTT (140829) Taq54 (3), 51 (4), 48 (5), and 45 (26) 
AC002457 AAAAACTATCCCATAATAAC (141001)  
C-ABL TTAATAAAGGGTTYGGAGAG (37356) Taq58 (34) 
U07563 CTAAAATAAAATAAAACAAACCTACA (37633)  
THBS1 GGAGAGAGGAGTTTAGATTGGTT (46325) BstUI 54 (34) 
AC037198 AATAAAAATTACTCCTAAAAAAC (46489)  
THBS2 ATTAGAAGGGGGATGATGTT (5571) TaqI 58 (37) 
U79410 ACCCTACAATACAAAATACTCC 5708)  
Myf3 TGTTGGAGAGGTTTGGAAAGG (9952) Rsa60 (3), 57 (4), 54(5), and 51(25) 
AF027148 AAAATCCRAAACCAATAAAAACAC (10141)  
CD10 TTYGGTTTTAGTTTGGAGTT (3665) BstUI 58 (3), 56(4), 54(5), and 52(26) 
X79438 CCCTTTAAACCTTTCTCCCT (3843)  
p73 methylated ACCCCGAACATCGACGTCCG  60 (34) 
(Ref. 21) GGACGTAGCGAAATCGGGGTTC   
p73 unmethylated ATCACAACCCCAAACATCAACATCCA  60 (34) 
(Ref. 21) AGGGGATGTAGTGAAATTGGGGTTT   
Gene/accesion no.Primers (coordinates)aRestriction enzyme/restriction sitesAnnealing temperatures (no. of cycles)
ER GGTTTTTGAGTTTTTTGTTTTG(300) BstUI 60 (3), 57 (4), 54 (5), and 51 (25) 
X03635 AACTTACTACTATCCAAATACACCTC(505)  
p15 GGAGTTTAAGGGGGTGGG (24747) BstUI 59 (37) 
AC000049 CCTAAATTACTTCTAAAAAAAAAC (24918)  
p16 GGTTTTGGYGAGGGTTGTTT (917) Taq58 (3), 56 (4), 54(5), and 52 (23) 
X94154 ACCCTATCCCTCAAATCCTCTAAAA (1078)  
MDR1 GTTATAGGAAGTTTGAGTTT (140829) Taq54 (3), 51 (4), 48 (5), and 45 (26) 
AC002457 AAAAACTATCCCATAATAAC (141001)  
C-ABL TTAATAAAGGGTTYGGAGAG (37356) Taq58 (34) 
U07563 CTAAAATAAAATAAAACAAACCTACA (37633)  
THBS1 GGAGAGAGGAGTTTAGATTGGTT (46325) BstUI 54 (34) 
AC037198 AATAAAAATTACTCCTAAAAAAC (46489)  
THBS2 ATTAGAAGGGGGATGATGTT (5571) TaqI 58 (37) 
U79410 ACCCTACAATACAAAATACTCC 5708)  
Myf3 TGTTGGAGAGGTTTGGAAAGG (9952) Rsa60 (3), 57 (4), 54(5), and 51(25) 
AF027148 AAAATCCRAAACCAATAAAAACAC (10141)  
CD10 TTYGGTTTTAGTTTGGAGTT (3665) BstUI 58 (3), 56(4), 54(5), and 52(26) 
X79438 CCCTTTAAACCTTTCTCCCT (3843)  
p73 methylated ACCCCGAACATCGACGTCCG  60 (34) 
(Ref. 21) GGACGTAGCGAAATCGGGGTTC   
p73 unmethylated ATCACAACCCCAAACATCAACATCCA  60 (34) 
(Ref. 21) AGGGGATGTAGTGAAATTGGGGTTT   
a

Y indicates C or T.

Table 2

Patient Characteristics

CharacteristicStudy cohort%
Median age (yrs) 39  
Male 48 60 
WBC count >30× 109/liter 37 46 
Platelet count >100× 109/liter 61 76 
Hemoglobin level <10 g/dl 51 63 
LDHa level >600 units/liter 73 91 
Karyotype   
 Diploid 19 23 
 Ph positive 19 23 
 T (8;14), t(8;2);t(8;22) 
 6q−, 14q+ 
 Insufficient metaphases 11 
 Hyperdiploid 
 Hypodiploid 2.5 
Immunophenotype:   
 Mature B cell 7.5 
 T cell 11 
 T-CALLA 3.75 
 Precursor B 8.75 
 CALLA 45 56 
 Null 6.25 
CR rate 73 91 
DFSb 32%  
OSb 34%  
CharacteristicStudy cohort%
Median age (yrs) 39  
Male 48 60 
WBC count >30× 109/liter 37 46 
Platelet count >100× 109/liter 61 76 
Hemoglobin level <10 g/dl 51 63 
LDHa level >600 units/liter 73 91 
Karyotype   
 Diploid 19 23 
 Ph positive 19 23 
 T (8;14), t(8;2);t(8;22) 
 6q−, 14q+ 
 Insufficient metaphases 11 
 Hyperdiploid 
 Hypodiploid 2.5 
Immunophenotype:   
 Mature B cell 7.5 
 T cell 11 
 T-CALLA 3.75 
 Precursor B 8.75 
 CALLA 45 56 
 Null 6.25 
CR rate 73 91 
DFSb 32%  
OSb 34%  
a

LDH, lactate dehydrogenase.

b

At 5 years.

Table 3

Distribution of methylation densities

GeneMethylation density
<2%2–14.9%15–49.9%>50%
N (%)N (%)N (%)N (%)
ER 39 (49) 12 (15) 20 (25) 9 (11) 
p15 41 (51) 20 (25) 14 (18) 5 (6) 
MDR1 37 (46) 7 (9) 19 (24) 17 (21) 
THBS1 60 (75) 4 (5) 12 (15) 4 (5) 
p16 74 (93) 4 (5) 1 (1) 1 (1) 
C-ABL 73 (91) 1 (1) 3 (4) 3 (4) 
THBS2 7 (9) 31 (39) 37 (46) 5 (6) 
MYOD 21 (26) 28 (35) 23 (29) 8 (10) 
CD10 64 (80) 8 (10) 5 (6) 3 (4) 
p73 methylateda 17 (21.2)    
GeneMethylation density
<2%2–14.9%15–49.9%>50%
N (%)N (%)N (%)N (%)
ER 39 (49) 12 (15) 20 (25) 9 (11) 
p15 41 (51) 20 (25) 14 (18) 5 (6) 
MDR1 37 (46) 7 (9) 19 (24) 17 (21) 
THBS1 60 (75) 4 (5) 12 (15) 4 (5) 
p16 74 (93) 4 (5) 1 (1) 1 (1) 
C-ABL 73 (91) 1 (1) 3 (4) 3 (4) 
THBS2 7 (9) 31 (39) 37 (46) 5 (6) 
MYOD 21 (26) 28 (35) 23 (29) 8 (10) 
CD10 64 (80) 8 (10) 5 (6) 3 (4) 
p73 methylateda 17 (21.2)    
a

For p73, MSP was used.

Table 4

Correlations between methylation densities for selected genes

r values between −0.2 and 2 were omitted.

GeneMDRTHBS1p73                  aTHBS2MYODCD10
ER 0.51 0.36 0.23 0.51 0.51  
MDR1  0.26 0.28 0.34 0.24  
THBS1    0.45 0.38 0.24 
p73    0.25 0.25  
THBS2     0.41 0.25 
GeneMDRTHBS1p73                  aTHBS2MYODCD10
ER 0.51 0.36 0.23 0.51 0.51  
MDR1  0.26 0.28 0.34 0.24  
THBS1    0.45 0.38 0.24 
p73    0.25 0.25  
THBS2     0.41 0.25 
a

κ statistic substituted for r value for p73.

Table 5

Correlations between DNA methylation and clinical biological characteristics

Methylation density (%)
ERp15p16MDR1C-ABLTHBS1p73THBS2MYF3CD10
Age (yrs)           
 >39 14.1 10 1.2 19.6 5.6 6.7 23% 20.5 14.3 3.3 
 <40 17.9 12.5 1.2 29.6 11.2 17% 21.3 19.5 5.7 
    P = 0.02       
Sex           
 Male 18.3 11.3 0.1 24.6 12 20.8% 22 18.5 4.6 
 Female 12.8 11.2 2.9 24.3 4.7 4.3 21.8% 18.9 16.3 4.3 
CGa           
 Diploid 15 16.6 0.4 28.8 0.1 23.5% 16.4 13.9 4.4 
 Ph+ 9.2 9.5 0.5 6.6 13.5 11.7% 16.4 11.9 0.12 
 Others 21.1 3.1 1.5 35.8 1.7 64.7% 22.6 18.7 3.8 
  P = 0.02  P = 0.03 P = 0.03      
Phenotype           
 T cell 29.5 16.4 1.2 29.3 0.4 28.8 20% 27.5 37.3 7.4 
 B cell 14.9 5.1  5.7 18.9  0% 25.6 15.4 1.3 
 CALLA 13.1 8.8 0.1 28.1 5.7 3.6 29% 18.5 14.7 0.6 
 Others 17.1 16.2 19.7 1.9 8.8 10% 21.5 16.1 13.3 
          P = 0.04 
Methylation density (%)
ERp15p16MDR1C-ABLTHBS1p73THBS2MYF3CD10
Age (yrs)           
 >39 14.1 10 1.2 19.6 5.6 6.7 23% 20.5 14.3 3.3 
 <40 17.9 12.5 1.2 29.6 11.2 17% 21.3 19.5 5.7 
    P = 0.02       
Sex           
 Male 18.3 11.3 0.1 24.6 12 20.8% 22 18.5 4.6 
 Female 12.8 11.2 2.9 24.3 4.7 4.3 21.8% 18.9 16.3 4.3 
CGa           
 Diploid 15 16.6 0.4 28.8 0.1 23.5% 16.4 13.9 4.4 
 Ph+ 9.2 9.5 0.5 6.6 13.5 11.7% 16.4 11.9 0.12 
 Others 21.1 3.1 1.5 35.8 1.7 64.7% 22.6 18.7 3.8 
  P = 0.02  P = 0.03 P = 0.03      
Phenotype           
 T cell 29.5 16.4 1.2 29.3 0.4 28.8 20% 27.5 37.3 7.4 
 B cell 14.9 5.1  5.7 18.9  0% 25.6 15.4 1.3 
 CALLA 13.1 8.8 0.1 28.1 5.7 3.6 29% 18.5 14.7 0.6 
 Others 17.1 16.2 19.7 1.9 8.8 10% 21.5 16.1 13.3 
          P = 0.04 
a

CG, cytogenetics.

Table 6

Impact of methylation on 5-year OS

Summary of the impact of methylation (first figure) versus unmethylation (second figure) on 5-year OS. Second row summarizes results for patients with Ph=negative disease.

ERp15p16p73MDR1c-ablMYF3CD10THBS1THBS2
All patients (n = 80) 43% 28% 50% 27% 51% 0% 35% 33% 66% 37% 
 vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. 
 28% 37% 43% 36% 33% 36% 35% 32% 28% 34% 
 P = 0.4 P = 0.4 P = 0.8 P = 0.5 0.09 P = 0.2 P = 0.7 P = 0.8 P = 0.02 P = 0.2 
Ph=negative patients (n = 57) 50% 26% 50% 30% 48% 0% 40% 63% 66% 45% 
 vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. 
 35% 46% 42% 46% 49% 51% 44% 41% 36% 42% 
 P = 0.5 P = 0.09 P = 0.8 P = 0.1 P = 0.4 P = 0.01 P = 0.9 P = 0.9 P = 0.1 P = 0.3 
ERp15p16p73MDR1c-ablMYF3CD10THBS1THBS2
All patients (n = 80) 43% 28% 50% 27% 51% 0% 35% 33% 66% 37% 
 vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. 
 28% 37% 43% 36% 33% 36% 35% 32% 28% 34% 
 P = 0.4 P = 0.4 P = 0.8 P = 0.5 0.09 P = 0.2 P = 0.7 P = 0.8 P = 0.02 P = 0.2 
Ph=negative patients (n = 57) 50% 26% 50% 30% 48% 0% 40% 63% 66% 45% 
 vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. 
 35% 46% 42% 46% 49% 51% 44% 41% 36% 42% 
 P = 0.5 P = 0.09 P = 0.8 P = 0.1 P = 0.4 P = 0.01 P = 0.9 P = 0.9 P = 0.1 P = 0.3 

We thank Dr. Ralph Arlinghaus for providing data on the Ph-positive cases, Jennifer Jones for sample distribution, and Sherry Pierce for data management and analysis.

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