Purpose: Aberrant methylation of the 5′ gene promoter regions is an epigenetic phenomenon that is the major mechanism for silencing of tumor suppressor genes in many cancer types. The aims of our study were (a) to compare the methylation profiles of the major forms of hematological malignancies and (b) to determine the methylation profile of monoclonal gammopathy of undetermined significance (MGUS) and compare it with that of multiple myeloma (MM).

Experimental Design: We compared the aberrant promoter methylation profile of 14 known or suspected tumor suppressor genes in leukemias (n = 48), lymphomas (n = 42), and MMs (n = 40). We also examined the methylation profile of MGUS (n = 20), a premalignant plasma cell dyscrasia. The genes studied represent five of the six “hallmarks of cancer.”

Results: Peripheral blood lymphocytes (n = 14) from healthy volunteers were negative for methylation of all genes, and methylation percentages in 41 nonmalignant tissues (peripheral blood mononuclear cells, bone marrows, and lymph nodes) from hematological patients were low (0–9%) for all 14 genes, confirming that methylation was tumor specific. Ten of the genes were methylated at frequencies of 29–68% in one or more tumor types, and the methylation indices (an indicator of overall methylation) varied from 0.25 to 0.34. With two exceptions, the methylation patterns of leukemias and lymphomas were similar. However, the pattern of MMs varied from the other tumor types for six genes. In general, the methylation pattern of MGUS was similar to that of MM, although the methylation frequencies were lower (the methylation index of MGUS was 0.15, and that of MM was 0.3). However, the methylation frequencies of six genes were significantly higher in MGUS than in control tissues. The relatively high frequencies of methylation in MGUS are consistent with it being a premalignant condition.

Conclusions: The three major forms of lymphoid/hematopoietic malignancies show overlapping but individual patterns of methylation.

Lymphomas, leukemias, and multiple myelomas (MMs) are cancers that originate in the hematopoietic or lymphoid tissues (“hematological malignancies”). An estimated 106,200 people in the United States will be diagnosed with lymphoma (61,000), leukemia (30,600), or MM (14,600) in 2003 (1). New cases of these hematological malignancies account for nearly 8% of all estimated new cancer cases in 2003. Hematological cancers, the second most common cause of cancer deaths, are expected to kill over 57,000 Americans in 2003 (1). Lymphoid/hematopoietic disorders are divided primarily into lymphomas, leukemias, and plasma cell dyscrasias. Lymphomas are further classified into two major subtypes, Hodgkin’s lymphoma and non-Hodgkin’s lymphoma, whereas leukemias are divided into two major subtypes, acute and chronic leukemia. Plasma cell dyscrasias are classified into two major subtypes: (a) MM and related malignancies; and (b) monoclonal gammopathy of undetermined significance (MGUS). MGUS is characterized by the presence of a homogenous monoclonal protein (M-component) in the serum of persons without evidence of MM, macroglobulinemia, amyloidosis, or a related plasma cell proliferative disorder. Patients with MGUS are at increased risk for progression to MM or a related plasma cell cancer (2). The risk of progression of MGUS to MM or related disorders has been reported to be about 1%/year (3) or higher (4, 5).

DNA methylation of the promoter region of genes has emerged as the major mechanism of inactivation of tumor suppressor genes [TSGs; (6)]. In many cases, aberrant methylation of the CpG island genes has been correlated with loss of gene expression, and DNA methylation provides an alternative pathway to gene deletion or mutation for the loss of TSG function (6, 7, 8). Markers for aberrant methylation may represent a promising avenue for monitoring the onset and progression of cancer. Aberrant promoter methylation has been described for several genes in various malignant diseases, and each tumor type may have its own distinct pattern of methylation (7, 9, 10).

Whereas the methylation of multiple genes has been studied in lymphomas and leukemias, relatively little is known about plasma cell dyscrasias. There are no reports comparing the methylation profile of multiple TSGs in the three major hematological malignancy types. Genes involved in the occurrence of MGUS and those associated with the malignant transformation from MGUS to MM have not been identified (11). The 14 genes were selected from five of the six “hallmarks of cancer” (12, 13) categories including some genes whose methylation status in hematological malignancies had not been previously studied (Table 1). The aims of our study were (a) to compare the methylation profiles of the major forms of hematological malignancies and (b) to determine the methylation profile of MGUS and compare it with that of MM.

Cell Lines.

Fourteen lymphoma, leukemia, and MM cell lines (Table 2) were either initiated by us (HCC3234, Hut 78, and NCI-H929) or obtained from American Type Culture Collection (Manassas, VA). They were grown in RPMI 1640 (Life Technologies, Inc., Rockville, MD) supplemented with 5% fetal bovine serum and incubated in 5% CO2 at 37°C.

Reverse Transcription-PCR for Gene Expression.

Expression of the genes was analyzed by reverse transcription-PCR. Total RNA was extracted from cell lines with Trizol (Life Technologies, Inc.) following the manufacturer’s instructions. The reverse transcription reaction was performed on 2 μg of total RNA with Superscript II First-Strand Synthesis using the oligo(dT) primer system (Life Technologies, Inc.). Primer sequences and conditions for reverse transcription-PCR product were as described previously (14, 15, 16, 17, 18, 19). The housekeeping gene GAPDH was used as an internal control to confirm the success of the reverse transcription reaction. PCR products were analyzed on 2% agarose gels.

5-Aza-2′-Deoxycytidine Treatment.

Cell lines with known gene promoter methylation were incubated in culture medium with methylation inhibitor 5-aza-2′-deoxycytidine at a concentration of 4 μm for 6 days, with medium changes on days 1, 3, and 5.

Clinical Samples.

Forty-two lymphomas (36 B-cell lymphomas including 7 Burkitt’s lymphomas and 6 T-cell lymphomas), 48 leukemias (27 acute lymphocytic leukemias, 11 acute myelogenous leukemias, 9 chronic lymphocytic leukemias, and 1 chronic myelogenous leukemia), 40 MMs, and 20 MGUSs were obtained from the Flow Cytometry Facility or from the Department of Pathology at the University of Texas Southwestern Medical Center or from the University Hospital, Vienna, Austria (Table 3). Control samples included peripheral blood lymphocytes from 14 healthy volunteers; 29 control samples (peripheral blood, lymph node, and bone marrow) from patients with hematopoietic malignancies in remission and 12 control samples from hematological patients without malignancies were also obtained from the Department of Pathology at the University of Texas Southwestern Medical Center (Table 4).

Methylation Assay.

Genomic DNA was isolated from frozen tissue by digestion with 100 μg/ml proteinase K followed by standard phenol-chloroform (1:1) extraction and ethanol precipitation. DNA was treated with sodium bisulfite as described previously (20). Treated DNA was purified by use of Wizard DNA Purification System (Promega Corp., Madison, WI), desulforated with 0.3 m NaOH, precipitated with ethanol, and resuspended in water. Modified DNA was stored at −80°C until use. References for methodology and gene information are summarized in Table 1. The methylation status of 13 genes was determined by methylation-specific PCR. For technical reasons, we used a combined restriction analysis for death-associated protein kinase (DAPK; Ref. 21). Negative control samples without DNA were included for each set of PCR. PCR products were analyzed on 2% agarose gels containing ethidium bromide.

Data Analysis.

Frequencies of methylation of two groups were compared using χ2 test or Fisher’s exact test. The methylation index (MI), a reflection of the methylation status of all of the genes tested, is defined as the total number of genes methylated divided by the total number of genes analyzed. To compare the extent of methylation for the panel of genes examined, we calculated the MIs for each case (22) and then determined the mean for the different groups. Statistical analysis of MI between two variables was performed using the Mann-Whitney U nonparametric test. For all tests, probability values of P < 0.05 were regarded as statistically significant. We investigated the relationship between the absence or presence of methylation in individual genes and covariates such as age, gender, and tumor types with multivariate logistic regression models.

Aberrant Promoter Methylation and Expression of CDH1, CDH13, DcR1, DcR2, TIMP3, CRBP1, and DAPK in Hematological Cancer Cell Lines.

For seven genes (CDH1, CDH13, DcR1, DcR2, TIMP3, CRBP1, and DAPK) whose methylation status in hematopoietic tumors has not been previously studied in detail, we correlated aberrant promoter methylation with loss of gene expression (Fig. 1,A) using a panel of 14 cell lines (Table 2). The overall concordance between loss or down-regulation of gene expression and aberrant methylation of these genes was 86% (range, 79–93%), confirming the relevance of our assay conditions.

5-Aza-2′-Deoxycytidine Treatment.

For the seven genes tested in cell lines, loss or down-regulation of gene expression in the 14 cell lines varied from 29% (DcR2) to 79% (CRBP1). Treatment with the methylation inhibitor 5-aza-2′-deoxycytidine restored expression in all methylated cell lines tested (Fig. 1 B).

Frequency of Methylation of 14 TSGs in Lymphoma, Leukemia, and MM Samples and Nonmalignant Hematological Tissues.

We examined the methylation status of 14 genes in control tissues and in the three major types of hematological malignancies (Figs. 2 and 3,A). The unmethylated form of p16, run as a control for DNA integrity, was detected in all of these samples. We examined the methylation status of 55 nonmalignant tissue samples (peripheral blood mononuclear cells, bone marrow, and lymph node) derived from three groups (normal volunteers, hematological patients without malignancy, and hematological patients with malignancies in remission). Because methylation patterns of these three control groups were similar, we pooled our data. Most of the control tissues (44 of 55, 80%) had no methylation of any gene. None of the 14 genes was methylated at a frequency of >10%, and the mean MI was 0.02 (Fig. 3 B).

In contrast, 90% of malignancies had at least one gene methylated, and the mean MIs were much higher (0.25–0.34). Ten of the genes were methylated at frequencies of 29–68% in one or more tumor types. In particular, five genes (CDH1, CDH13, DAPK, CRBP1, and RARβ) were frequently methylated (>30%) in all tumor types. In general, the methylation patterns of lymphomas and leukemias were similar, with eight genes methylated at frequencies of >20% (CDH1, CDH13, DAPK, CRBP1, p15, DcR1, RARβ, and TIMP3; Fig. 3,A). Significant differences in frequencies between lymphomas and leukemias were present for only two genes (DAPK and CRBP1), although the mean MI for lymphomas (0.34) was higher than that for leukemias (0.25; P = 0.04; Fig. 3 A). Whereas there was a tendency for more methylation in acute leukemia than in chronic leukemia and in B-cell lymphoma than in T-cell lymphoma, the mean MIs of these groups were not significantly different [except for acute leukemia compared with chronic leukemia (P = 0.04)].

Whereas the mean MI of MM (0.30) was not significantly different from that of lymphomas or leukemias, the methylation frequencies in MM of six genes were significantly different from those of one or both of the other two types of malignancies. In particular, DcR1 and p16 were methylated at greater frequencies in MM, whereas RASSF1A methylation was completely absent, and TIMP3 was infrequently methylated in MM (Fig. 3 A).

Frequency of Methylation in MGUS Samples.

Of the 20 MGUS samples, 17 (85%) had one or more genes methylated, and the mean MI and the frequencies for six genes were higher than those in control tissues (Fig. 3,B). Whereas the percentage of MGUS cases with at least one gene methylated was similar to that of MM cases, the mean MI of MGUS cases (0.15) was lower than that of MMs (0.30; P = 0.0005). For most genes, the methylation frequencies were higher in MM cases, and for four genes (CDH1, DcR1, CDH13, and DAPK), these differences were significant. In particular, DAPK was methylated in 36% of MMs and in 0% of MGUS cases (Fig. 3 B). Of interest, three MGUS cases had MIs in the range of MMs (0.29–0.36).

Tumor Patterns of Methylation.

We used logistic regressions that model the absence or presence of methylation in individual genes with different tumor types. These studies indicated that the methylation patterns of leukemias and lymphomas were similar, and in general, the methylation patterns of MMs and MGUS were similar, although MM tended to be a stronger predictor of methylation for many genes.

Effects of Age and Gender on Methylation Status.

Because aging has been reported to be one factor in methylation and shows some degree of tissue type dependency, we examined the ages of our control (mean age, 46.3 years) and malignant (mean age, 49.6 years) groups. In the control group, there was no significant relationship between methylation frequencies of any gene or the MI and age. In addition, there was no relationship between methylation frequencies and tumor patient gender. Univariate and multivariate logistic regression analyses confirmed these findings.

Effects of Tumor Cell Percentage on Methylation Status.

Because the number of tumor cells present in specimens of hematological malignancies varies greatly, the possibility of false negative methylation results has to be considered. The methylation-specific PCR assays we used were sufficiently sensitive to detect one methylated allele in the presence of 103–104 nonmethylated alleles (20, 23). Because 87 (67%) of the tumor specimens were received from the flow cytometry laboratory, an accurate estimate was available of the number of tumor cells present. Whereas the percentage ranged from 0.1% to 91% (46.3%), there was no relationship between methylation frequencies and the tumor cell percentage.

Correlation between Individual TSGs in Hematological Malignancies.

For the genes frequently methylated (>20%) in one or more malignancies, we determined whether there was any correlation between the methylation status of paired genes. We found a tight correlation between the cell adhesion molecules CDH1 and CDH13 (P = 0.0001), and between the decoy receptors DcR1 and DcR2 (P < 0.0001).

Previous studies have described the importance of DNA methylation in human cancers and focused on regions of the genome that may have functional significance resulting from the extinction of gene activity. Whereas most individual cancers have several, perhaps hundreds, of methylated genes, the methylation profiles of individual tumor types are characteristic (7, 9, 23).

We studied the methylation profile of a panel of 14 known or potential TSGs in the three major tumor types of hematological malignancies. Recently, Hanahan and Weinberg (12) described six hallmarks that a cell has to acquire to become malignant: (a) limitless replicative potential; (b) self-sufficiency in growth signals; (c) insensitivity to growth-inhibitory signals; (d) evasion of programmed cell death; (e) sustained angiogenesis; and (f) tissue invasion and metastasis (12, 13). The 14 genes we studied were selected from five of these categories. For the seven genes whose methylation status in hematological malignancies had not been previously studied in detail, we confirmed that our methylation assay conditions were correlated with gene silencing in cells lines derived from hematological malignancies.

We studied appropriate control tissues (peripheral blood mononuclear cells, bone marrows, and lymph nodes) from three groups of controls: healthy volunteers; hematological patients without cancer; and hematological cancer patients in remission. Methylation of all of the genes in these tissues was absent or present at very low frequencies. In some tissues, especially those of the gastrointestinal tract, methylation of certain genes may be related to age (24). However, we detected no relationship between methylation and age in either tumor or control tissues. These studies confirmed the tumor-specific nature of the methylation assays used. Because of the relative sensitivity of the methylation assays, our results were not biased in samples containing a low percentage of tumor cells.

There were statistically significant differences between individual types of malignancies and nonmalignant tissues (P < 0.0001). Thirteen genes showed tumor specificity of methylation, and 10 of the genes were methylated at frequencies of 29–68% in one or more tumor types. The mean MI of leukemias was lower than those of the other two tumor types. With two exceptions (DAPK and CRBP1), the methylation patterns of leukemias and lymphomas were similar. However, the pattern of MMs varied from that of the other tumor types for six genes. Somewhat surprisingly, the major subtypes of leukemias and lymphomas had similar patterns, except that acute leukemias tended to have more methylation than chronic cases.

For the genes frequently methylated (>20%) in one or more malignancies, we determined whether there was any correlation between the methylation status of paired genes. We found a tight correlation between the cell adhesion molecules CDH1 and CDH13 and between the decoy receptors for tumor necrosis factor-related apoptosis-inducing ligand DcR1 and DcR2.

A finding of considerable interest was the finding that 85% of MGUS cases had one or more genes methylated. Methylation in MGUS cases was significantly higher than that in control tissues but significantly lower than that in MM cases. Three MGUS cases (15%) had MIs similar to that of MMs. Currently, there is no method for determining which MGUS cases are at increased risk of progression to MM or other malignancy. Our findings suggest that methylation of certain genes (CDH1, CDH13, DAPK, and DcR1) may aid in this prediction. However, because of the limited number of cases studied and the lengthy observation period required, further work will be necessary to test this hypothesis.

Of the 14 genes we studied, 8 (CDH1, CDH13, p16, p15, CRBP1, RARβ, DAPK, and p73) had previously been studied in one or more forms of hematological malignancies (Refs. 16, 17, 19, and 25, 26, 27, 28, 29, 30; Table 1). Our findings, with one exception, fall within the published ranges. For DAPK, we have previously published that the original methylation-specific PCR method did not target the promoter region of the gene and that methylation did not correlate with loss of gene expression (21). Using a combined restriction analysis method we devised previously and validated (21), we found somewhat lower frequencies in MM/lymphomas (34–62%) than the figure reported by Katzenellenbogen et al.(19) and Ng et al.(32) using the methylation-specific PCR methodology.

To our knowledge, six of the genes (APC, TIMP3, RIZ1, DcR1, DcR2, and RASSF1A) we studied have not previously been investigated in detail in hematological malignancies. APC is a well-studied molecule involved wnt signaling, whereas TIMP3 is an inhibitor of matrix metalloproteinases. RIZ1 is a retinoblastoma protein-interacting zinc finger gene and a member of the nuclear histone methyltransferase superfamily. DcR1 and DcR2 are decoy receptors for tumor necrosis factor-related apoptosis-inducing ligand and, apparently paradoxically, are methylated and silenced in many tumor type (15, 32). RASSF1A is an important new TSG frequently silenced via methylation in many tumor types (33, 34, 35, 36). Of these six genes, DcR1, DcR2, and TIMP3 were methylated at frequencies of >20% in one or more hematological malignancies. Whereas RASSF1A is frequently methylated in many epithelial and some pediatric cancers, methylation frequencies in hematological malignancies were very low.

Our study represents the first comprehensive comparison of the profile of the three major forms of lymphopoietic/hematopoietic tumors. The profiles of these three tumor types showed important similarities and differences. Of particular interest, the majority of MGUS cases had methylation of one or multiple genes. Our findings are of biological and possible clinical interest.

Grant support: Grant 5U01CA8497102 from the Early Detection Research Network, National Cancer Institute (Bethesda, MD).

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.

Requests for reprints: Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, 6000 Harry Hines Boulevard, Dallas, TX 75390-8593. Phone: (214) 648-4921; Fax: (214) 648-4940; E-mail: Adi.Gazdar@UTSouthwestern.edu

Fig. 1.

A, representative examples of reverse transcription-PCR and methylation-specific PCR (CDH13 and DcR1) or combined restriction analysis (DAPK) of three genes in lymphoma, leukemia, and multiple myeloma cell lines. Expression of the housekeeping gene GAPDH was run as a control for RNA integrity. P, positive control; N, negative control. The presence of a single band indicates a positive reaction for reverse transcription-PCR and methylation-specific PCR assays. In most instances, there was concordance between the presence of methylation and loss of expression. In a few instances (open arrowheads), both a methylated band and gene expression were present, suggesting the presence of both methylated and unmethylated alleles. For the combined restriction analysis assay, the amplicon product (arrow) is digested by two methylation-specific enzymes, TaqI (shown) or BSTUI (data not shown). If methylation is present at one or more CpG sites in the amplicon, the product is digested to yield multiple bands. Both enzymes yielded identical results, confirming their specificity. B, representative examples of the effect of 5-aza-2′-deoxycytidine treatment on restoring gene expression in methylated cell lines. Treatment with 5-aza-2′-deoxycytidine restored expression of the three genes (CDH13, DcR1, and DAPK) illustrated. Expression of the housekeeping gene GAPDH was run as a control for RNA integrity. +, with 5-aza-2′-deoxycytidine treatment; −, without 5-aza-2′-deoxycytidine treatment.

Fig. 1.

A, representative examples of reverse transcription-PCR and methylation-specific PCR (CDH13 and DcR1) or combined restriction analysis (DAPK) of three genes in lymphoma, leukemia, and multiple myeloma cell lines. Expression of the housekeeping gene GAPDH was run as a control for RNA integrity. P, positive control; N, negative control. The presence of a single band indicates a positive reaction for reverse transcription-PCR and methylation-specific PCR assays. In most instances, there was concordance between the presence of methylation and loss of expression. In a few instances (open arrowheads), both a methylated band and gene expression were present, suggesting the presence of both methylated and unmethylated alleles. For the combined restriction analysis assay, the amplicon product (arrow) is digested by two methylation-specific enzymes, TaqI (shown) or BSTUI (data not shown). If methylation is present at one or more CpG sites in the amplicon, the product is digested to yield multiple bands. Both enzymes yielded identical results, confirming their specificity. B, representative examples of the effect of 5-aza-2′-deoxycytidine treatment on restoring gene expression in methylated cell lines. Treatment with 5-aza-2′-deoxycytidine restored expression of the three genes (CDH13, DcR1, and DAPK) illustrated. Expression of the housekeeping gene GAPDH was run as a control for RNA integrity. +, with 5-aza-2′-deoxycytidine treatment; −, without 5-aza-2′-deoxycytidine treatment.

Close modal
Fig. 2.

Representative examples of methylation-specific PCR analyses of methylated form or combined restriction analysis of three genes (CDH13, DcR1, and DAPK) in tumor samples. The unmethylated form of p16 (p16UM) was run as an internal control for bisulfite treatment. Lymphoma samples, Ly 1—Ly 8; leukemia samples, Le 1—Le 8; multiple myeloma samples, MM 1—MM 8; P, positive control; N, negative control.

Fig. 2.

Representative examples of methylation-specific PCR analyses of methylated form or combined restriction analysis of three genes (CDH13, DcR1, and DAPK) in tumor samples. The unmethylated form of p16 (p16UM) was run as an internal control for bisulfite treatment. Lymphoma samples, Ly 1—Ly 8; leukemia samples, Le 1—Le 8; multiple myeloma samples, MM 1—MM 8; P, positive control; N, negative control.

Close modal
Fig. 3.

Comparison of frequencies of aberrant methylation and mean methylation indices in tumor samples (A) or in multiple myeloma, monoclonal gammopathy of undetermined significance, and nonmalignant hematological tissues (B). Ps are shown when there was a significant difference between two groups.

Fig. 3.

Comparison of frequencies of aberrant methylation and mean methylation indices in tumor samples (A) or in multiple myeloma, monoclonal gammopathy of undetermined significance, and nonmalignant hematological tissues (B). Ps are shown when there was a significant difference between two groups.

Close modal
Table 1

Summary data of genes tested

Gene symbolGene nameChromosomal locationHallmark categoryaPrevious studies (ref. no.)Ref. no. for methodology
CDH1 E-cadherin 16q22 Tissue invasion and metastasis Leb (17, 37, 38)  39  
CDH13 H-cadherin 16q24 Tissue invasion and metastasis Le (26)  14  
APC Adenomatous polyposis coli gene 5q21 Tissue invasion and metastasis None  40  
TIMP3 Tissue inhibitor of metalloproteinase-3 22q12–13 Tissue invasion and metastasis None  41  
p16                  INK4A Cyclin-dependent kinase inhibitor 2A 9p21 Limitless replicative potential Ly (27, 28, 42, 43)  20  
    Le (27, 44, 45)  
    MM, MGUS (29, 46)  
p15                  INK4B Cyclin-dependent kinase inhibitor 2B 9p21 Limitless replicative potential Ly (27, 28, 43, 47)  20  
    Le (27, 48, 49, 50)  
    MM.MGUS (29)  
CRBP1 Cellular retinol-binding protein 1 3q21–22 Limitless replicative potential Ly (16)  16  
RIZ1 Rb-interacting zinc finger gene 1 1p36 Limitless replicative potential None  51  
RARβ Retinoic acid receptor β gene 3p24 Limitless replicative potential Ly (30)  52  
    Le (53)  
DcR1 Decoy receptor 1 8p22 Apoptosis None  15  
DcR2 Decoy receptor 2 8p22 Apoptosis None  15  
DAPK Death-associated protein kinase 9q34 Apoptosis Ly (19)  21  
    Le (19)  
    MM (32)  
RASSF1A RAS association domain family protein 1A 3p21 Self-sufficiency in growth signals None  34  
p73 p53-related protein p73 1p36 Sustained angiogenesis Ly (30, 31, 54)  31  
    Le (31, 53, 55)  
Gene symbolGene nameChromosomal locationHallmark categoryaPrevious studies (ref. no.)Ref. no. for methodology
CDH1 E-cadherin 16q22 Tissue invasion and metastasis Leb (17, 37, 38)  39  
CDH13 H-cadherin 16q24 Tissue invasion and metastasis Le (26)  14  
APC Adenomatous polyposis coli gene 5q21 Tissue invasion and metastasis None  40  
TIMP3 Tissue inhibitor of metalloproteinase-3 22q12–13 Tissue invasion and metastasis None  41  
p16                  INK4A Cyclin-dependent kinase inhibitor 2A 9p21 Limitless replicative potential Ly (27, 28, 42, 43)  20  
    Le (27, 44, 45)  
    MM, MGUS (29, 46)  
p15                  INK4B Cyclin-dependent kinase inhibitor 2B 9p21 Limitless replicative potential Ly (27, 28, 43, 47)  20  
    Le (27, 48, 49, 50)  
    MM.MGUS (29)  
CRBP1 Cellular retinol-binding protein 1 3q21–22 Limitless replicative potential Ly (16)  16  
RIZ1 Rb-interacting zinc finger gene 1 1p36 Limitless replicative potential None  51  
RARβ Retinoic acid receptor β gene 3p24 Limitless replicative potential Ly (30)  52  
    Le (53)  
DcR1 Decoy receptor 1 8p22 Apoptosis None  15  
DcR2 Decoy receptor 2 8p22 Apoptosis None  15  
DAPK Death-associated protein kinase 9q34 Apoptosis Ly (19)  21  
    Le (19)  
    MM (32)  
RASSF1A RAS association domain family protein 1A 3p21 Self-sufficiency in growth signals None  34  
p73 p53-related protein p73 1p36 Sustained angiogenesis Ly (30, 31, 54)  31  
    Le (31, 53, 55)  
a

Hallmark categories are defined by Hanahan and Weinberg (12), and the category selection was from Widschwendter and Jones (13).

b

Le, leukemia; Ly, lymphoma; MM, multiple myeloma; MGUS, monoclonal gammopathy of undetermined significance.

Table 2

Cell lines

NameATCC#Cell line typeSubtype
BC-1 CRL-2230 Lymphoma  B cell 
BC-3 CRL-2277 Lymphoma  B cell 
RL CRL-2261 Lymphoma  B cell 
HuT 78 TIB-161 Lymphoma  T cell 
Daudi CCL-213 Lymphoma Burkitt B cell 
Raji CCL-86 Lymphoma Burkitt B cell 
HCC3234  Lymphoma Burkitt B cell 
CEM CRL-2264 Leukemia ALLa T cell 
Jurkat TIB-152 Leukemia ALL T cell 
KG-1 CCL-246 Leukemia AML  
K-562 CCL-243 Leukemia CML  
U266B1 TIB-196 MM  B cell 
MC/CAR CRL-8083 MM  B cell 
NCI-H929 CRL-9068 MM  B cell 
NameATCC#Cell line typeSubtype
BC-1 CRL-2230 Lymphoma  B cell 
BC-3 CRL-2277 Lymphoma  B cell 
RL CRL-2261 Lymphoma  B cell 
HuT 78 TIB-161 Lymphoma  T cell 
Daudi CCL-213 Lymphoma Burkitt B cell 
Raji CCL-86 Lymphoma Burkitt B cell 
HCC3234  Lymphoma Burkitt B cell 
CEM CRL-2264 Leukemia ALLa T cell 
Jurkat TIB-152 Leukemia ALL T cell 
KG-1 CCL-246 Leukemia AML  
K-562 CCL-243 Leukemia CML  
U266B1 TIB-196 MM  B cell 
MC/CAR CRL-8083 MM  B cell 
NCI-H929 CRL-9068 MM  B cell 
a

ALL, acute lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; MM, multiple myeloma.

Table 3

Tumorsa,b

Lymphoma (n = 42)  
 B-cell n = 36 
 T-cell n = 6 
Leukemia (n = 48)  
 ALLc n = 27 
 AML n = 11 
 CML n = 9 
 CLL n = 1 
MM n = 40 
MGUS n = 25 
Lymphoma (n = 42)  
 B-cell n = 36 
 T-cell n = 6 
Leukemia (n = 48)  
 ALLc n = 27 
 AML n = 11 
 CML n = 9 
 CLL n = 1 
MM n = 40 
MGUS n = 25 
a

The male:female ratio of our tumor cases was 3:2, and their mean age was 49.6 years (range, 0–87 years).

b

Lymphoma specimens consisted of 27 lymph nodes, 11 bone marrows, 3 peripheral blood mononuclear cells, and 1 pleural effusion. Leukemia specimens consisted of 22 bone marrows, 13 peripheral blood mononuclear cells and 13 lymph nodes. All of multiple myeloma and monoclonal gammopathy of undetermined significance specimens consisted of bone marrows.

c

ALL, acute lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; CLL, chronic lymphocytic leukemias; MM, multiple myeloma; MGUS, monoclonal gammopathy of undetermined significance.

Table 4

Controls

Normal lymphocytes n = 14 
Patients in remission (n = 29)  
 Peripheral blood n = 24 
 Lymph node n = 2 
 Bone marrow n = 3 
Patients without Lya Le, MM (n = 12)  
 Peripheral blood n = 4 
 Lymph node n = 1 
 Bone marrow n = 7 
Normal lymphocytes n = 14 
Patients in remission (n = 29)  
 Peripheral blood n = 24 
 Lymph node n = 2 
 Bone marrow n = 3 
Patients without Lya Le, MM (n = 12)  
 Peripheral blood n = 4 
 Lymph node n = 1 
 Bone marrow n = 7 
a

Ly, lymphoma; Le, leukemia; MM, multiple myeloma.

1
Jemal A, Murray T, Samuels A, et al Cancer statistics, 2003.
CA-Cancer J Clin
,
53
:
5
-26,  
2003
.
2
Kyle RA. Monoclonal gammopathy of undetermined significance (MGUS).
Bailliere’s Clin Haematol
,
8
:
761
-81,  
1995
.
3
Kyle RA, Therneau TM, Rajkumar SV, et al A long-term study of prognosis in monoclonal gammopathy of undetermined significance.
N Engl J Med
,
346
:
564
-9,  
2002
.
4
Kyle RA. “Benign” monoclonal gammopathy. A misnomer?.
JAMA
,
251
:
1849
-54,  
1984
.
5
Baldini L, Guffanti A, Cesana BM, et al Role of different hematologic variables in defining the risk of malignant transformation in monoclonal gammopathy.
Blood
,
87
:
912
-8,  
1996
.
6
Jones PA, Baylin SB. The fundamental role of epigenetic events in cancer.
Nat Rev Genet
,
3
:
415
-28,  
2002
.
7
Costello JF, Fruhwald MC, Smiraglia DJ, et al Aberrant CpG-island methylation has non-random and tumour-type-specific patterns.
Nat Genet
,
24
:
132
-8,  
2000
.
8
Baylin SB, Esteller M, Rountree MR, et al Aberrant patterns of DNA methylation, chromatin formation and gene expression in cancer.
Hum Mol Genet
,
10
:
687
-92,  
2001
.
9
Esteller M, Corn PG, Baylin SB, Herman JG. A gene hypermethylation profile of human cancer.
Cancer Res
,
61
:
3225
-9,  
2001
.
10
Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future.
Oncogene
,
21
:
5427
-40,  
2002
.
11
Hallek M, Bergsagel PL, Anderson KC. Multiple myeloma: increasing evidence for a multistep transformation process.
Blood
,
91
:
3
-21,  
1998
.
12
Hanahan D, Weinberg RA. The hallmarks of cancer.
Cell
,
100
:
57
-70,  
2000
.
13
Widschwendter M, Jones PA. DNA methylation and breast carcinogenesis.
Oncogene
,
21
:
5462
-82,  
2002
.
14
Sato M, Mori Y, Sakurada A, Fujimura S, Horii A. The H-cadherin (CDH13) gene is inactivated in human lung cancer.
Hum Genet
,
103
:
96
-101,  
1998
.
15
van Noesel MM, van Bezouw S, Salomons GS, et al Tumor-specific down-regulation of the tumor necrosis factor-related apoptosis-inducing ligand decoy receptors DcR1 and DcR2 is associated with dense promoter hypermethylation.
Cancer Res
,
62
:
2157
-61,  
2002
.
16
Esteller M, Guo M, Moreno V, et al Hypermethylation-associated Inactivation of the cellular retinol-binding-protein 1 gene in human cancer.
Cancer Res
,
62
:
5902
-5,  
2002
.
17
Corn PG, Smith BD, Ruckdeschel ES, et al E-cadherin expression is silenced by 5′ CpG island methylation in acute leukemia.
Clin Cancer Res
,
6
:
4243
-8,  
2000
.
18
Bian J, Wang Y, Smith MR, et al Suppression of in vivo tumor growth and induction of suspension cell death by tissue inhibitor of metalloproteinases (TIMP)-3.
Carcinogenesis (Lond)
,
17
:
1805
-11,  
1996
.
19
Katzenellenbogen RA, Baylin SB, Herman JG. Hypermethylation of the DAP-kinase CpG island is a common alteration in B-cell malignancies.
Blood
,
93
:
4347
-53,  
1999
.
20
Herman JG, Graff JR, Myohanen S, Nelkin BD, Baylin SB. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands.
Proc Natl Acad Sci USA
,
93
:
9821
-6,  
1996
.
21
Toyooka S, Toyooka KO, Miyajima K, et al Epigenetic down-regulation of death-associated protein kinase in lung cancers.
Clin Cancer Res
,
9
:
3034
-41,  
2003
.
22
Maruyama R, Toyooka S, Toyooka KO, et al Aberrant promoter methylation profile of bladder cancer and its relationship to clinicopathological features.
Cancer Res
,
61
:
8659
-63,  
2001
.
23
Virmani AK, Muller C, Rathi A, et al Aberrant methylation during cervical carcinogenesis.
Clin Cancer Res
,
7
:
584
-9,  
2001
.
24
Zochbauer-Muller S, Fong KM, Virmani AK, et al Aberrant promoter methylation of multiple genes in non-small cell lung cancers.
Cancer Res
,
61
:
249
-55,  
2001
.
25
Issa JP. The epigenetics of colorectal cancer.
Ann N Y Acad Sci
,
910
:
140
-53, discussion 153–5 
2000
.
26
Roman-Gomez J, Castillejo JA, Jimenez A, et al Cadherin-13, a mediator of calcium-dependent cell-cell adhesion, is silenced by methylation in chronic myeloid leukemia and correlates with pretreatment risk profile and cytogenetic response to interferon α.
J Clin Oncol
,
21
:
1472
-9,  
2003
.
27
Herman JG, Civin CI, Issa JP, et al Distinct patterns of inactivation of p15INK4B and p16INK4A characterize the major types of hematological malignancies.
Cancer Res
,
57
:
837
-41,  
1997
.
28
Baur AS, Shaw P, Burri N, et al Frequent methylation silencing of p15(INK4b) (MTS2) and p16(INK4a) (MTS1) in B-cell and T-cell lymphomas.
Blood
,
94
:
1773
-81,  
1999
.
29
Guillerm G, Gyan E, Wolowiec D, et al p16(INK4a) and p15(INK4b) gene methylations in plasma cells from monoclonal gammopathy of undetermined significance.
Blood
,
98
:
244
-6,  
2001
.
30
Siu LL, Chan JK, Wong KF, Choy C, Kwong YL. Aberrant promoter CpG methylation as a molecular marker for disease monitoring in natural killer cell lymphomas.
Br J Haematol
,
122
:
70
-7,  
2003
.
31
Corn PG, Kuerbitz SJ, van Noesel MM, et al Transcriptional silencing of the p73 gene in acute lymphoblastic leukemia and Burkitt’s lymphoma is associated with 5′ CpG island methylation.
Cancer Res
,
59
:
3352
-6,  
1999
.
32
Ng MH, To KW, Lo KW, et al Frequent death-associated protein kinase promoter hypermethylation in multiple myeloma.
Clin Cancer Res
,
7
:
1724
-9,  
2001
.
33
Shivapurkar N, Toyooka S, Toyooka K, et al Aberrant methylation of TRAIL decoy receptor genes is frequent in multiple tumor types.
Int J Cancer
,
109
:
786
-92,  
2004
.
34
Burbee DG, Forgacs E, Zochbauer-Muller S, et al Epigenetic inactivation of RASSF1A in lung and breast cancers and malignant phenotype suppression.
J Natl Cancer Inst (Bethesda)
,
93
:
691
-9,  
2001
.
35
Dammann R, Li C, Yoon JH, et al Epigenetic inactivation of a RAS association domain family protein from the lung tumour suppressor locus 3p21.3.
Nat Genet
,
25
:
315
-9,  
2000
.
36
Agathanggelou A, Honorio S, Macartney DP, et al Methylation associated inactivation of RASSF1A from region 3p21.3 in lung, breast and ovarian tumours.
Oncogene
,
20
:
1509
-18,  
2001
.
37
Melki JR, Vincent PC, Clark SJ. Concurrent DNA hypermethylation of multiple genes in acute myeloid leukemia.
Cancer Res
,
59
:
3730
-40,  
1999
.
38
Melki JR, Vincent PC, Brown RD, Clark SJ. Hypermethylation of E-cadherin in leukemia.
Blood
,
95
:
3208
-13,  
2000
.
39
Graff JR, Herman JG, Myohanen S, Baylin SB, Vertino PM. Mapping patterns of CpG island methylation in normal and neoplastic cells implicates both upstream and downstream regions in de novo methylation.
J Biol Chem
,
272
:
22322
-9,  
1997
.
40
Tsuchiya T, Tamura G, Sato K, et al Distinct methylation patterns of two APC gene promoters in normal and cancerous gastric epithelia.
Oncogene
,
19
:
3642
-6,  
2000
.
41
Bachman KE, Herman JG, Corn PG, et al Methylation-associated silencing of the tissue inhibitor of metalloproteinase-3 gene suggest a suppressor role in kidney, brain, and other human cancers.
Cancer Res
,
59
:
798
-802,  
1999
.
42
Martinez-Delgado B, Fernandez-Piqueras J, Garcia MJ, et al Hypermethylation of a 5′ CpG island of p16 is a frequent event in non-Hodgkin’s lymphoma.
Leukemia (Baltimore)
,
11
:
425
-8,  
1997
.
43
Garcia MJ, Martinez-Delgado B, Cebrian A, et al Different incidence and pattern of p15INK4b and p16INK4a promoter region hypermethylation in Hodgkin’s and CD30-positive non-Hodgkin’s lymphomas.
Am J Pathol
,
161
:
1007
-13,  
2002
.
44
Toyota M, Kopecky KJ, Toyota MO, et al Methylation profiling in acute myeloid leukemia.
Blood
,
97
:
2823
-9,  
2001
.
45
Chim CS, Tam CY, Liang R, Kwong YL. Methylation of p15 and p16 genes in adult acute leukemia: lack of prognostic significance.
Cancer (Phila)
,
91
:
2222
-9,  
2001
.
46
Kramer A, Schultheis B, Bergmann J, et al Alterations of the cyclin D1/pRb/p16(INK4A) pathway in multiple myeloma.
Leukemia (Baltimore)
,
16
:
1844
-51,  
2002
.
47
Martinez-Delgado B, Robledo M, Arranz E, et al Hypermethylation of p15/ink4b/MTS2 gene is differentially implicated among non-Hodgkin’s lymphomas.
Leukemia (Baltimore)
,
12
:
937
-41,  
1998
.
48
Herman JG, Jen J, Merlo A, Baylin SB. Hypermethylation-associated inactivation indicates a tumor suppressor role for p15INK4B.
Cancer Res
,
56
:
722
-7,  
1996
.
49
Aggerholm A, Guldberg P, Hokland M, Hokland P. Extensive intra- and interindividual heterogeneity of p15INK4B methylation in acute myeloid leukemia.
Cancer Res
,
59
:
436
-41,  
1999
.
50
Wong IH, Ng MH, Huang DP, Lee JC. Aberrant p15 promoter methylation in adult and childhood acute leukemias of nearly all morphologic subtypes: potential prognostic implications.
Blood
,
95
:
1942
-9,  
2000
.
51
Du Y, Carling T, Fang W, et al Hypermethylation in human cancers of the RIZ1 tumor suppressor gene, a member of a histone/protein methyltransferase superfamily.
Cancer Res
,
61
:
8094
-9,  
2001
.
52
Virmani AK, Rathi A, Zochbauer-Muller S, et al Promoter methylation and silencing of the retinoic acid receptor-β gene in lung carcinomas.
J Natl Cancer Inst (Bethesda)
,
92
:
1303
-7,  
2000
.
53
Chim CS, Wong SY, Kwong YL. Aberrant gene promoter methylation in acute promyelocytic leukaemia: profile and prognostic significance.
Br J Haematol
,
122
:
571
-8,  
2003
.
54
Martinez-Delgado B, Melendez B, Cuadros M, et al Frequent inactivation of the p73 gene by abnormal methylation or LOH in non-Hodgkin’s lymphomas.
Int J Cancer
,
102
:
15
-9,  
2002
.
55
Garcia-Manero G, Daniel J, Smith TL, et al DNA methylation of multiple promoter-associated CpG islands in adult acute lymphocytic leukemia.
Clin Cancer Res
,
8
:
2217
-24,  
2002
.