Genome-wide DNA methylation profiles in precancerous conditions and cancers - PubMed (original) (raw)
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Genome-wide DNA methylation profiles in precancerous conditions and cancers
Yae Kanai. Cancer Sci. 2010 Jan.
Abstract
Alterations of DNA methylation, which result in chromosomal instability and silencing of tumor-related genes, are among the most consistent epigenetic changes observed in human cancers. Analysis of tissue specimens has revealed that DNA methylation alterations participate in multistage carcinogenesis, even from the early and precancerous stages, especially in association with chronic inflammation and/or persistent viral infection, such as chronic hepatitis or liver cirrhosis resulting from infection with hepatitis B or C virus. DNA methylation alterations can account for the histological heterogeneity and clinicopathological diversity of human cancers. Overexpression of DNA methyltransferase 1 is not a secondary result of increased cell proliferative activity, but is significantly correlated with accumulation of DNA hypermethylation in CpG islands of tumor-related genes. Alteration of DNA methyltransferase 3b splicing may result in chromosomal instability through DNA hypomethylation in pericentromeric satellite regions. Genome-wide analysis of DNA methylation status has revealed that the DNA methylation profile at the precancerous stage is basically inherited by the corresponding cancers developing in individual patients. DNA methylation status is not simply altered at the precancerous stage; rather, DNA methylation alterations at the precancerous stage may confer vulnerability to further genetic and epigenetic alterations, generate more malignant cancers, and thus determine patient outcome. Therefore, genome-wide DNA methylation profiling may provide optimal indicators for carcinogenetic risk estimation and prognostication, and thus provide an avenue for cancer prevention and therapy on an individual basis.
Figures
Figure 1
Overexpression of DNA methyltransferase (DNMT) 1 protein during multistage urothelial carcinogenesis. (a) Specimen obtained by radical cystectomy for multiple urothelial carcinomas (UCs) of the urinary bladder, bilateral ureters, and prostatic urethra. UCs are clinically remarkable because of their multicentricity and tendency to recur: synchronously or metachronously multifocal UCs often develop in individual patients.( 38 ) A possible mechanism for such multiplicity is the “field effect.” Even non‐cancerous urothelia showing no remarkable histological changes obtained from patients with UCs can be considered precancerous, because they may be exposed to carcinogens in the urine. (b) Immunohistochemical examination for DNMT1 and proliferating cell nuclear antigen (PCNA) in tissue specimens. The incidence of nuclear DNMT1 immunoreactivity had already increased in non‐cancerous urothelia showing no remarkable histological changes obtained from patients with UCs (NC), where the PCNA labeling index had not yet increased, compared to that in normal urothelia obtained from patients without UCs (Cont), indicating that DNMT1 overexpression preceded any increase of cell proliferative activity.( 56 ) The intensity of nuclear DNMT1 immunoreactivity was further increased in UCs.( 56 )
Figure 2
DNA methylation profiles in precancerous conditions and renal cell carcinomas (RCCs). (a) Bacterial artificial chromosome array‐based methylated CpG island amplification (BAMCA) data for tissue samples obtained from patients with RCCs (arrowheads). Using unsupervised hierarchical clustering analysis based on BAMCA data for samples of their non‐cancerous renal tissue, patients with RCCs were clustered into two subclasses, Clusters AN and BN. ( 72 ) Clinicopathologically aggressive RCCs were accumulated in Cluster BN, and the overall survival rate of patients in Cluster BN was significantly lower than that of patients in Cluster AN.( 72 ) Using unsupervised hierarchical clustering analysis based on BAMCA data for their RCCs, patients were clustered into two subclasses, Clusters AT and BT.( 72 ) Clinicopathologically aggressive clear cell RCCs were accumulated in Cluster BT, and the overall survival rate of patients in Cluster BT was significantly lower than that of patients in Cluster AT.( 72 ) (b) Correlation between DNA methylation profiles of precancerous conditions and those of RCCs. Cluster BN was completely included in Cluster BT (left panel). The majority of the bacterial artificial chromosome (BAC) clones, 724 in all, significantly discriminating Cluster BN from Cluster AN, also discriminated Cluster BT from Cluster AT.( 72 ) In 311 of the 724 BAC clones, where the average signal ratio of Cluster BN was higher than that of Cluster AN, such as Clone R1 in the middle panel, the average signal ratio of Cluster BT was also higher than that of Cluster AT without exception.( 72 ) In 413 of the 724 BAC clones, where the average signal ratio of Cluster BN was lower than that of Cluster AN, such as Clone R2 in the middle panel, the average signal ratio of Cluster BT was also lower than that of Cluster AT without exception.( 72 ) As shown in the scattergram of the signal ratios in non‐cancerous renal tissue samples and RCCs for all examined patients for a representative BAC clone, Clone R3, the DNA methylation status of the non‐cancerous renal tissue was basically inherited by the corresponding RCC in individual patients (right panel).( 72 )
Figure 3
Significance of DNA methylation alterations at the precancerous stage. Chronic inflammation, persistent infection with viruses or other pathogenic microorganisms, cigarette smoking, exposure to chemical carcinogens, and other unknown factors may participate in the establishment of particular DNA methylation profiles, such as Cluster BN in Fig. 2. Such DNA methylation alterations in precancerous conditions may not occur randomly, but may be prone to further accumulation of epigenetic and genetic alterations (regional DNA hypermethylation of C‐type CpG islands and copy number alterations were accumulated in Cluster BT in Fig. 2),( 72 ) thus generating more malignant cancers, such as the renal cell carcinomas in patients belonging to Cluster BT.
Figure 4
Risk estimation of hepatocellular carcinoma (HCC) development based on DNA methylation status. (a) Examples of scan images and scattergrams of signal ratios in normal liver tissue obtained from patients without HCCs (C) and non‐cancerous liver tissue obtained from patients with HCCs (N). In N samples, many bacterial artificial chromosome (BAC) clones showed DNA hypo‐ or hypermethylation compared to C samples.( 75 ) (b) Four patterns of DNA methylation alterations seen in BAC clones during multistage hepatocarcinogenesis: (i) DNA methylation alterations occurred at the chronic hepatitis and liver cirrhosis stage, and DNA methylation status did not alter in HCCs from the chronic hepatitis and liver cirrhosis stage; (ii) DNA methylation alterations occurred at the chronic hepatitis and liver cirrhosis stage and further altered in HCCs; (iii) although DNA methylation alterations occurred at the chronic hepatitis and liver cirrhosis stage, the DNA methylation status returned to normal in HCCs; and (iv) DNA methylation alterations occurred only in HCCs. In order to establish criteria for carcinogenetic risk estimation, we focused on BAC clones whose DNA methylation status was inherited by HCCs from the precancerous stage (groups i and ii), whereas group iii may only reflect inflammation and/or fibrosis, and group iv may participate only in the malignant progression stage. (c) Two‐dimensional hierarchical clustering analysis using BAC clones that were selected as the top 25 for which DNA methylation status was able to discriminate N from C with sufficient sensitivity and specificity by Wicoxon test and the support vector machine algorithm.( 75 ) C and N samples in the learning cohort were successfully subclassified into different subclasses without any error.( 75 ) (d) Scattergrams of the signal ratios in C and N samples in the learning cohort for representative BAC clones, Clone H1 and Clone H2. Using the cut‐off values (CV) in each panel, N samples in the learning cohort were discriminated from C samples with sufficient sensitivity and specificity.( 75 ) Based on a combination of DNA methylation status for the 25 BAC clones, the criteria for carcinogenetic risk estimation were established. Using these criteria, the sensitivity and specificity for diagnosis of N samples in the learning cohort as being at high risk of carcinogenesis were both 100%.( 75 ) The sensitivity and specificity in the validation cohort were both 96%, and thus the criteria were successfully validated.( 75 )
Figure 5
Prognostication of patients with HCC development based on DNA methylation status. (a) Two‐dimensional hierarchical clustering analysis using 41 bacterial artificial chromosome (BAC) clones selected as those for which DNA methylation status was able to discriminate a poor‐outcome group (P), who suffered recurrence within 6 months and died within a year after hepatectomy, from a favorable‐outcome group (F), who survived for more than 4 years after hepatectomy, with sufficient sensitivity and specificity by Wilcoxon test.( 75 ) F and P patients in the learning cohort were successfully subclassified into different subclasses without any error.( 75 ) (b) Scattergrams of the signal ratios in F and P patients in the learning cohort for representative BAC clones, Clone H26 and Clone H27. Using the cut‐off values (CV) in each panel, P patients in the learning cohort were discriminated from F patients with 100% sensitivity and specificity.( 75 ) Based on a combination of the DNA methylation status of the 41 BAC clones, criteria for prognostication were established. (c) The cancer‐free and overall survival rates of patients with HCCs in the validation cohort. Patients with HCCs satisfying the criteria for 32 or more BAC clones showed significantly poorer outcome than patients with HCCs satisfying the criteria for less than 32 BAC clones.( 75 )
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