Abstract

Epigenetic abnormalities, such as aberrant methylation of CpG islands, are inherited over cell divisions, and play important roles in carcinogenesis. Aberrant methylation of CpG islands specific to tumor cells can be used as a marker to detect cancer cells or cancer-derived DNA, taking advantage of the high sensitivity of methods to detect aberrant methylation. Methylations of specific genes or methylation patterns of groups of genes were found to be associated with responses to chemotherapeutics and prognosis. Methylation in non-cancerous tissues is now attracting attention as a tumor risk marker, and is emerging as a target for cancer prevention. Epigenetic alterations are potentially reversible. The use of DNA demethylating agents has turned out to be effective for hematological malignancies, and is being tested in solid tumors. Histone deacetylase inhibitors and methods for gene-specific epigenetic modification are being developed. Application of epigenetics to cancer diagnostics and therapeutics, and possibly to cancer prevention, is coming into clinics.

The clinical usefulness of cancer epigenetics is becoming clear. Many kinds of tumor-specific aberrant DNA methylation have been identified (1,2), and these are being tested as tumor markers (3,4). Demethylating agents have proven to be effective as chemotherapeutic drugs (5,6). The heritable nature and the plasticity of epigenetic information, which could sound contradictory, underlie these clinical applications. In this review, we would like briefly to describe basic aspects of epigenetics, and then focus on the current status of its clinical applications.

WHAT DNA METHYLATION IS, AND ITS ABNORMALITY IN CANCERS

Epigenetic information is defined as heritable information other than the DNA sequence (7), and is represented by methylation of cytosines at CpG sites (Fig. 1). When a CpG site is methylated, cytosines on both DNA strands are methylated (full methylation) and, when a CpG site is not methylated, neither strand is methylated. At DNA replication, the methylated status is transmitted to daughter DNAs by maintenance DNA methyltransferase (DNMT), which is present at a replication fork and recognizes hemi-methylated CpG sites (1,2). Therefore, the methylated or unmethylated status of CpG sites is faithfully copied into daughter DNA over DNA replications in somatic cells. The methylated or unmethylated status of an individual CpG site is inherited with an error rate between 0.1 and 4 × 10−2/CpG site/replication (8,9).

Figure 1.

Inheritance of DNA methylation status. Although a newly synthesized DNA strand (shown in gray) does not have methyl groups (M), maintenance DNA methyltransferase at the replication fork transfers a methyl group to the newly synthesized strand. Maintenance DNA methyltransferase transfers methyl groups only at hemi-methylated CpG sites, and thus unmethylated CpG sites are kept unmethylated.

Figure 1.

Inheritance of DNA methylation status. Although a newly synthesized DNA strand (shown in gray) does not have methyl groups (M), maintenance DNA methyltransferase at the replication fork transfers a methyl group to the newly synthesized strand. Maintenance DNA methyltransferase transfers methyl groups only at hemi-methylated CpG sites, and thus unmethylated CpG sites are kept unmethylated.

Although the density of CpG sites in the genome is very low, there are clusters of CpG sites, known as ‘CpG islands’ (CGIs), which are generally kept unmethylated (Fig. 2). When a CGI in the promoter region of a gene is methylated, expression of the gene is repressed (1,10). The repression is caused by changes in chromatin structures due to binding of specific proteins to methylated DNA and by decreased affinity of some transcriptional factors for methylated CpG sites. In addition to the effect on gene transcription, DNA methylation is also involved in genomic imprinting, which refers to parental origin-specific expression of a gene, and formation of a chromatin domain (10).

Figure 2.

Methylation status of promoter CGIs and gene expression. Promoter CGIs are generally kept unmethylated, and the downstream gene is expressed (upper panel). When the CGI is methylated, it causes repression of mRNA expression (lower panel). This mode of inactivation is observed in various tumor-suppressor genes. Filled and open circles represent methylated and unmethylated CpG sites, respectively.

Figure 2.

Methylation status of promoter CGIs and gene expression. Promoter CGIs are generally kept unmethylated, and the downstream gene is expressed (upper panel). When the CGI is methylated, it causes repression of mRNA expression (lower panel). This mode of inactivation is observed in various tumor-suppressor genes. Filled and open circles represent methylated and unmethylated CpG sites, respectively.

Due to their heritable nature, alterations of epigenetic information are deeply involved in carcinogenesis, along with genetic alterations (1,2). Aberrant methylation of CGIs in promoter regions of tumor-suppressor genes, such as RB, p16, VHL, hMLH1, E-cadherin and BRCA1, is known to be involved in their inactivation in various cancers, including stomach, colon, liver, breast, uterine, renal and hematological tumors. At the same time, genome-overall hypomethylation, mainly due to hypomethylation of repetitive sequences, is present in most tumor cells (7). Hypomethylation is known to lead to genomic instability and tumor formation (11). It also causes aberrant expression of some cancer-testis antigen genes, such as MAGEs (12). Aberrant methylation of imprinted genes can disturb imprinting (loss of imprinting; LOI). LOI of insulin-like growth factor 2 (IGF2) is causally involved in Wilms tumors and colorectal cancers through its overexpression (7).

APPLICATION OF ABERRANT DNA METHYLATION TO CANCER DIAGNOSTICS

Aberrant DNA methylation can be applied to cancer diagnostics in three ways. First, if aberrant methylation of some CGIs is specifically present in cancer cells, it can be used to detect cancer cells in biopsy samples or cancer-derived free DNA in plasma. Secondly, if aberrant methylation of some CGIs is associated with a disease phenotype, such as prognosis, responses to chemotherapies or occurrence of adverse effects, it can be used as a marker to predict the phenotype. Thirdly, if aberrant methylation of some CGIs in non-cancerous tissue is associated with a risk for cancer development, it can be used as a cancer risk marker.

Advantages of DNA Methylation over Mutations as a Marker

As a marker to detect cancer cells or cancer-derived DNA, DNA methylation has several advantages over mutations. First, incidences of aberrant methylation of specific CGIs are higher than those of mutations (1315), and such methylation can be discovered by genome-wide screening procedures (16,17). Secondly, aberrant methylation of a DNA molecule can be sensitively detected, even when it is embedded in an excess amount of normal DNA molecules, by methylation-specific polymerase chain reaction (PCR) (MSP) (18) and quantitative MSP (4). Thirdly, detection of aberrant methylation is technically simple. Aberrant methylation usually takes place in an all-or-nothing manner, and it can be detected using only one set of PCR primers. On the other hand, mutations can take place in various regions of a gene, and many primer sets are necessary for complete analysis. Finally, some aberrant methylation is observed in early stages of carcinogenesis and even in non-neoplastic tissues. For example, methylation of non-core regions of p16 is observed in pulmonary hyperplasia (17%), dysplasia (24%), and lung carcinoma in situ (50%) (19).

Detection of Cancer Cells in Body Fluid

For detection of cancer cells in body fluids, such as urine, sputum, bronchoalveolar lavage (BAL), mammary aspiration fluids, saliva and stools, a high sensitivity is essential. When intact cancer cells are present in a specimen, detection of aberrant methylation will assist cytology by giving further confirmation. When cancer cells are degraded or very few in number, DNA diagnosis will be especially powerful to detect DNA molecules derived from cancer cells with a high sensitivity.

Many examples have been reported for this type of use (Table 1). Methylation of p16, O6-methylguanine DNA methyltransferase (MGMT), retinoic acid receptor beta (RARβ), death-associated protein kinase 1 (DAPK), hMLH1, E-cadherin, APC and RASSF1A was analyzed in sputum and BAL to detect lung cancers (1928). Methylation of glutathione S-transferase P1 (GSTP1) was analyzed in urine and ejaculate to detect prostate cancers (2931). Methylation of cyclin D2, RARβ, Twist, GSTP1, p16, p14, RASSF1A and DAPK was analyzed in mammary aspirate to detect breast cancers (32,33). Methylation of p16, DAPK and MGMT was analyzed in saliva to detect head and neck cancers (34). Methylation of DAPK, RARβ, E-cadherin, APC, RASSF1A and p14 was analyzed in urine to detect bladder cancers (35,36). Methylation of SFRP2 was analyzed in stools to detect colorectal cancers (37). Most of these studies report concordance between methylation analysis and conventional cytology, or better sensitivity of the former.

Table 1.

Detection of cancer cells in various medical specimens by DNA methylation

Tumor
 
Specimen
 
Gene
 
Incidence
 
(%)
 
References
 
Lung Sputum (smoker) p16 3/7 43 (19
 Sputum (non-smoker) p16 5/26 19 (19
 Bronchoalveolar lavage (BAL) p16 12/50 24 (20
 Sputum p16 8/10 80 (21
 Sputum MGMT 5/10 50 (21
 Sputum/BAL p16 26/51 51 (22
 BAL p16 4/17 24 (23
 BAL p16 14/68 21 (24
  RARβ 48/60 80 (24
  DAPK 14/26 54 (24
  MGMT 6/18 33 (24
 Pleural lavage p16 3/99 (25
 Sputum (cytology negative) hMLH1 11/29 38 (26
 Sputum p16 71/95 75 (27
 BAL E-cadherin, APC, MGMT, RASSF1A, GSTP1, p16, RARβ and ARF 21/31 68 (28
Prostate Urine GSTP1 4/11 36 (29
 Ejaculate GSTP1 4/8 50 (29
 Urine GSTP1 6/28 21 (30
 Urine GSTP1 21/69 30 (31
Breast Ductal lavage fluid Cyclin D2, RARβ and Twist 4/6 67 (32
 Nipple aspirate GSTP1, RARβ, p16, p14ARF, RASSF1A and DAPK 18/22 82 (33
Head and neck Saliva p16, DAPK and MGMT 11/30 37 (34
Bladder Urine RARβ, DAPK, E-cadherin and p16 20/22 91 (35
 Urine APC, RASSF1A and p14ARF 39/45 87 (36
Colon and rectum Stool SFRP2 9/10 90 (37
Tumor
 
Specimen
 
Gene
 
Incidence
 
(%)
 
References
 
Lung Sputum (smoker) p16 3/7 43 (19
 Sputum (non-smoker) p16 5/26 19 (19
 Bronchoalveolar lavage (BAL) p16 12/50 24 (20
 Sputum p16 8/10 80 (21
 Sputum MGMT 5/10 50 (21
 Sputum/BAL p16 26/51 51 (22
 BAL p16 4/17 24 (23
 BAL p16 14/68 21 (24
  RARβ 48/60 80 (24
  DAPK 14/26 54 (24
  MGMT 6/18 33 (24
 Pleural lavage p16 3/99 (25
 Sputum (cytology negative) hMLH1 11/29 38 (26
 Sputum p16 71/95 75 (27
 BAL E-cadherin, APC, MGMT, RASSF1A, GSTP1, p16, RARβ and ARF 21/31 68 (28
Prostate Urine GSTP1 4/11 36 (29
 Ejaculate GSTP1 4/8 50 (29
 Urine GSTP1 6/28 21 (30
 Urine GSTP1 21/69 30 (31
Breast Ductal lavage fluid Cyclin D2, RARβ and Twist 4/6 67 (32
 Nipple aspirate GSTP1, RARβ, p16, p14ARF, RASSF1A and DAPK 18/22 82 (33
Head and neck Saliva p16, DAPK and MGMT 11/30 37 (34
Bladder Urine RARβ, DAPK, E-cadherin and p16 20/22 91 (35
 Urine APC, RASSF1A and p14ARF 39/45 87 (36
Colon and rectum Stool SFRP2 9/10 90 (37

Detection of Cancer-derived Free DNA in Plasma/Serum

The presence of free DNA derived from cancer cells in plasma/serum of cancer patients was reported back in the 1970s (38). Such free DNA is considered to be derived from apoptotic and necrotic cancer cells (39). Although the amounts of free DNA in plasma/serum were elevated in cancer patients, detection of cancer-specific alterations was necessary to obtain enough specificity suitable for clinical use. For this purpose, tumor-specific mutations were initially used (40). However, this turned out not to be useful because the exact location of a mutation within a gene is usually unknown and specific amplification of DNA molecules with a mutation embedded in an excess amount of normal molecules was difficult.

On the other hand, aberrant methylation of a CGI can be more readily detected (4,18), and many reports describe authors detecting cancer-derived DNA using plasma/serum by MSP or quantitative MSP in lung (23,25,27,4144), head and neck (4548), esophagal (49,50), breast (5155), liver (5658), colorectal (5963), stomach (6467), prostate (29) and bladder (68,69) cancers and melanoma (70) (Table 2). For non-small cell lung cancers (NSCLCs), 15 of 22 cancers were positive for methylation of at least one of p16, DAPK, GSTP1 and MGMT, and 11 of the 15 cases had aberrantly methylated DNA in the serum (41). Detection rates of NSCLCs varied from 6 to 76% using p16 methylation (23,25,27,42,43). A surprisingly high detection rate of 76% was reported using APC methylation (44), but no follow-up study has been reported.

Table 2.

Detection of cancer-derived DNA in plasma/serum by DNA methylation

Tumor
 
Specimen
 
Gene
 
Quantitative analysis
 
Incidence (%)
 
Control
 
References
 
NSCLC p16*, DAPK, GSTP1, and MGMT No 11/22 (50) N/A (41
 p16 No 1/10 (10) N/A (23
 p16 No 77/105 (73) N/A (42
 p16 No 12/35 (34) 0/15# (43
 p16 No 2/33 (6) N/A (25
 p16 No 103/136 (76) N/A (27
 APC Yes 42/89 (47) 0/50## (44
Head and neck p16, DAPK and MGMT No 21/50 (42) N/A (45
 DAPK No 6/16 (38) N/A (46
 p16 and p15 Yes 13/20 (65) 4/24 (47
 E-cadherin, p16, DAPK, p15 and RASSF1A Yes 29/41 (71) 4/43 (48
Esophageal AdC APC Yes 13/52 (25) 0/54 (49
Esophageal SCC APC Yes 2/32 (6) 0/54 (49
 p16 No 7/38 (18) N/A (71
Breast p16 No 5/35 (14) N/A (51
 p16 No 6/43 (14) N/A (52
 p16 No 4/41 (10) N/A (53
 p16 and E-cadherin No 9/36 (25) N/A (54
 APC, RASSF1A and DAPK No 26/34 (76) N/A (55
Liver P/S p16 No 13/22 (59) 0/48# (56
 P/S p16 and p15 No 17/25 (68) 0/55# (57
 P/S p16 Yes 23/29 (80) 0/35# (58
Colon and rectum hMLH1 No 3/19 (16) N/A (59
 P16 No 14/52 (27) 0/42 (60
 p16 No 21/58 (36) N/A (61
 p16 No 13/94 (14) N/A (62
 DAPK No 3/18 (17) N/A (63
Stomach DAPK, E-cadherin, GSTP1, p16 and p15 No 45/54 (83) 0/30# (64
 p16 No 6/60 (10) 0/16# (65
 p16 and E-cadherin No 40/109 (37) 0/10 (66
 p16, E-cadherin and RARβ No 18/41 (44) 0/?# (67
Prostate P/S GSTP1 No 23/32 (72) N/A (29
Bladder p14 No 13/27 (48) N/A (68
 p16 No 2/27 (7) N/A (68
 p16 No 19/86 (22) 1/49# (69
Melanoma RASSF1A, MGMT and RARβ No 9/31 (29) N/A (70
Tumor
 
Specimen
 
Gene
 
Quantitative analysis
 
Incidence (%)
 
Control
 
References
 
NSCLC p16*, DAPK, GSTP1, and MGMT No 11/22 (50) N/A (41
 p16 No 1/10 (10) N/A (23
 p16 No 77/105 (73) N/A (42
 p16 No 12/35 (34) 0/15# (43
 p16 No 2/33 (6) N/A (25
 p16 No 103/136 (76) N/A (27
 APC Yes 42/89 (47) 0/50## (44
Head and neck p16, DAPK and MGMT No 21/50 (42) N/A (45
 DAPK No 6/16 (38) N/A (46
 p16 and p15 Yes 13/20 (65) 4/24 (47
 E-cadherin, p16, DAPK, p15 and RASSF1A Yes 29/41 (71) 4/43 (48
Esophageal AdC APC Yes 13/52 (25) 0/54 (49
Esophageal SCC APC Yes 2/32 (6) 0/54 (49
 p16 No 7/38 (18) N/A (71
Breast p16 No 5/35 (14) N/A (51
 p16 No 6/43 (14) N/A (52
 p16 No 4/41 (10) N/A (53
 p16 and E-cadherin No 9/36 (25) N/A (54
 APC, RASSF1A and DAPK No 26/34 (76) N/A (55
Liver P/S p16 No 13/22 (59) 0/48# (56
 P/S p16 and p15 No 17/25 (68) 0/55# (57
 P/S p16 Yes 23/29 (80) 0/35# (58
Colon and rectum hMLH1 No 3/19 (16) N/A (59
 P16 No 14/52 (27) 0/42 (60
 p16 No 21/58 (36) N/A (61
 p16 No 13/94 (14) N/A (62
 DAPK No 3/18 (17) N/A (63
Stomach DAPK, E-cadherin, GSTP1, p16 and p15 No 45/54 (83) 0/30# (64
 p16 No 6/60 (10) 0/16# (65
 p16 and E-cadherin No 40/109 (37) 0/10 (66
 p16, E-cadherin and RARβ No 18/41 (44) 0/?# (67
Prostate P/S GSTP1 No 23/32 (72) N/A (29
Bladder p14 No 13/27 (48) N/A (68
 p16 No 2/27 (7) N/A (68
 p16 No 19/86 (22) 1/49# (69
Melanoma RASSF1A, MGMT and RARβ No 9/31 (29) N/A (70

NSCLC, non-small cell lung cancers; AdC, adenocarcinoma; SCC, squamous cell carcinoma; P, plasma; S, serum; N/A: not analyzed.

*

Regions potentially methylated in non-cancer cells

#

age-match unknown

##

age-matched.

Esophageal adenocarcinomas and squamous cell carcinomas were detected using APC and p16 methylation (49,71). In particular, a high plasma level of methylated APC was associated with reduced patient survival (P = 0.016) (49). For breast cancers, only 10–25% of cases were detected by p16 methylation (5154), but use of APC, RASSF1A and DAPK increased the detection rate up to 76% (55). Unfortunately, none of these studies analyzed control cases to assess the specificity of ‘aberrant’ methylation. For stomach cancers, a high sensitivity with a good specificity was achieved using methylation of DAPK, E-cadherin, GSTP1, p16 and p15 (64), and follow-up studies have been reported (6567).

Most studies analyzed a region within a CGI that can be methylated without affecting gene expression (72) and tends to be methylated in non-cancerous tissues. Also, traditional MSP is susceptible to false-positive results. These points raise a concern that DNA that appeared to be derived from cancer cells was in fact derived from non-cancerous cells that had methylation of the region analyzed, and that cancer cases had a heavy burden of such cells. In other words, there is a possibility that cancer predisposition, which is associated with the presence of cancers, was detected by the presence of methylated DNA in the peripheral blood. Quantitative measurements of methylated DNA molecules and appropriate age-matched control cases are essential from this aspect. The underlying mechanism(s) needs to be carefully examined, as already pointed out (73), and identification of novel CGIs that are frequently methylated with a high specificity is important.

DNA Methylation as a Marker for Clinical Responses

It has been reported that the methylation status of individual genes or patterns of multiple genes can be associated with clinically useful information, such as disease prognoses and responses to therapeutics. Methylation of DAPK, a positive mediator of apoptosis, is associated with early recurrence in bladder cancers [P < 0.001; hazards ratio (HR) = 7.01] (74). Methylation of MGMT, a gene involved in repair of O6-methylguanine, is a useful predictor of the responsiveness of tumors to alkylating agents in gliomas (P < 0.01), and is associated with good survival in patients treated with multidrug regimens (P < 0.001; HR = 9.5) (75). Similar results were also observed in patients with diffuse large B-cell lymphoma (P = 0.01; HR = 2.8) (76). Methylation of RASSF1A and/or APC in serum DNA is associated with poor prognosis of breast cancer cases (P = 0.007; HR = 5.7) (77). Methylation of multiple CGIs in neuroblastoma tissues is a strong predictor of clinical outcome (P < 0.0001; HR = 22.1) (78). When compared with an expression microarray, which is also a useful tool (79), methylation analysis has an advantage that it can be performed using chemically stable DNA, not RNA.

DNA Methylation as a Cancer Risk Marker

Cancer risk assessment is a promising use of aberrant methylation. Aberrant methylation of p16 can be an early event in lung cancer, and can be detected not only in lung cancer patients but also in cancer-free smokers, who were considered to be at high risk (19). There was no difference in the incidence of aberrant p16 methylation between current and former smokers (17–35%), but none of seven never-smokers had methylation (80,81).

Loss of IGF2 imprinting in colonic mucosae is associated with an elevated risk of colorectal cancers (82,83), and LOI in peripheral lymphocyte was also associated with an increased risk (84). A study using a mouse model for loss of IGF2 imprinting showed that LOI caused less differentiation of normal intestinal epithelium (85). Use of aberrant methylation as a cancer risk marker seems to be a promising field.

APPLICATION OF EPIGENETICS TO CANCER THERAPEUTICS

Epigenetic information is heritable, but has plasticity. Dynamic erasure and writing of epigenetic information take place in specific genes during embryonic development (86). This makes us expect that epigenetic information can be modified once we know how to do it. So far, several classes of drugs, including inhibitors of DNMTs and histone deacetylases (HDACs), are known to modify epigenetic information in a manner that is not specific to genes. At the same time, gene-specific methods are under development.

Demethylation by 5-Aza-2′-deoxycytidine, Zebularine and Others

DNMTs, especially DNMT1, play important roles in maintaining CpG methylation (87) (Fig. 1), and their inhibitors are known to induce hypomethylation. In particular, an inhibitor of DNMT1, 5-aza-2′-deoxycitidine (5-aza-dC, decitabine) (Fig. 3A), forms irreversible covalent bonds with DNMT1 after its incorporation into DNA, and induces degradation of DNMT1. DNA replication in its absence is known to result in hypomethylation (88).

Figure 3.

Structures of (A) 5-aza-2′-deoxycytidine (5-aza-dC, decitabine) and (B) zebularine.

Figure 3.

Structures of (A) 5-aza-2′-deoxycytidine (5-aza-dC, decitabine) and (B) zebularine.

In clinics in around 1990, 5-aza-dC was used at high doses, 75–100 mg/m2/day, as one of the cytotoxic drugs (89). However, in around 2000, the demethylating effect of 5-aza-dC regained attention, and lower doses of 5-aza-dC, 45–50 mg/m2/day, were tested, and turned out to be effective in hematological malignancies (90). In 2004, a phase I study of low-dose prolonged administration of 5-aza-dC, 5–20 mg/m2/day, showed that low doses were as or more effective than higher doses (6). Myelosuppression was the major adverse effect, but the treatment was well tolerated. Since inhibition of DNMT1 can be achieved by a low dose of 5-aza-dC and cell divisions are required for its action, use of low doses, which is different from most cytotoxic agents, and continuous administration were considered to be important to achieve the maximum demethylating effect on cancer cells. One of the shortcomings of 5-aza-dC is its short half-life. To overcome this point, zebularine is currently under development (91) (Fig. 3B). Zebularine is also useful because it can be administered orally or intraperitoneally.

Encouraged by the promising results obtained with 5-aza-dC, other chemicals with demethylating activity are eagerly being searched for, and a convenient assay system for demethylating agents has been developed (92). Among chemicals already in clinical use or in food, procainamide, procaine and (−)-epigallocatechin-3-gallate (EGCG) are shown to have demethylating activity (9395). Considering that some aberrant DNA methylation is present in early stages of carcinogenesis, there is a possibility that such demethylating agents may be useful for cancer prevention. Further study using suitable animal models is necessary.

MG98 is a phosphorothioate antisense oligodeoxynucleotide, and is a specific inhibitor of DNA methyltransferase mRNA. One partial response was documented in a patient with renal cell carcinoma treated at 80 mg/m2, and phase II trials are ongoing (96).

Histone Deacetylase (HDAC) Inhibitors

Histone modification is closely associated with DNA methylation status, and is important for gene regulation (10). Histone acetylation and methylation of histone H3 Lys4 are associated with active gene transcription, and methylation of histone H3 Lys9 is associated with gene repression. Modification of other amino acid residues is also attracting attention, and interaction between DNA methylation and various histone modifications is now being investigated.

Among these, histone deacetylation, mediated by three classes of HDACs, is well studied as a target of therapeutics (97,98). Specific inhibition of HDACs by their inhibitors, such as trichostatin A (TSA), leads to hyperacetylation of histones, and cell cycle inhibitors, such as p21WAF1, are upregulated (98). Various HDAC inhibitors have been developed for therapeutic purposes, and tumor cells are known to show higher sensitivity than normal cells for unidentified mechanisms. HDAC inhibitors are also reported to be effective even in non-proliferating tumor cells in vitro (99). For reversal of gene silencing, addition of HDAC inhibitors to 5-aza-dC is known to be effective. Phase I/II trials are now under way for solid tumors (98).

Target-specific Epigenetic Modification

The drugs described above do not have gene specificity. Non-specific demethylation has the risk of inducing demethylation of normally methylated sequences, such as retrotransposons, and thus retrotranspositions. Non-specific methylation has the risk of inducing silencing of tumor-suppressor genes. Therefore, development of a gene-specific method is important, and efforts are being made in laboratories.

First, synthetic transcription factors that have specific targets are reported to induce activation or repression in a domain-dependent manner (100). Overexpression of IGF2 is involved in cancer development. It is reported that a methylated oligonucleotide complementary to a region of the IGF2 promoter induced DNA methylation at the locus and diminished IGF2 mRNA. Moreover, animals with cancer cells with high IGF2 expression survived longer when treated with the oligonucleotide (101). Secondly, short interfering RNAs, which targeted the E-cadherin promoter, were reported to be able to induce DNA methylation of the region (102). If this can be applied to genes whose overexpression is tumorigenic, such as IGF2, it will be a powerful tool to induce gene-specific methylation.

EPILOGUE

Epigenetic changes potentially useful as tumor markers are now under clinical trials, and useful markers will be selected. Risk prediction and cancer prevention are also promising areas of epigenetics. A high efficacy of demethylating agents was reported mainly in hematological malignancies based on new protocols (lower dose and/or longer exposure), and their use is now being tried for solid tumors. Gene-specific epigenetic modification is being intensively investigated. We believe that application of epigenetics to cancer prevention, diagnostics and therapeutics will contribute to cancer control.

The authors are grateful to Drs Eriko Okochi-Takada, Junichi Furuta and Takao Maekita for critical reading of the manuscript.

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