A seven-gene CpG-island methylation panel predicts breast cancer progression.

BACKGROUND: DNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors. METHODS: CpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes. RESULTS: Seven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63-79 %, 53-84 % specificities, positive predictive values of 59-83 % and negative predictive values of 63-80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression. CONCLUSIONS: Our findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings.

Commonality and differences of methylation signatures in the plasma of patients with pancreatic cancer and colorectal cancer.

Profiling of DNA methylation status of specific genes is a way to screen for colorectal cancer (CRC) and pancreatic cancer (PC) in blood. The commonality of methylation status of cancer-related tumor suppressor genes between CRC and PC is largely unknown. Methylation status of 56 cancer-related genes was compared in plasma of patients in the following cohorts: CRC, PC and healthy controls. Cross validation determined the best model by area under ROC curve (AUC) to differentiate cancer methylation profiles from controls. Optimal preferential gene methylation signatures were derived to differentiate either cancer (CRC or PC) from controls. For CRC alone, a three gene signature (CYCD2, HIC and VHL) had an AUC 0.9310, sensitivity (Sens) = 0.826, specificity (Spec) = 0.9383. For PC alone, an optimal signature consisted of five genes (VHL, MYF3, TMS, GPC3 and SRBC), AUC 0.848; Sens = 0.807, Spec = 0.666. Combined PC and CRC signature or "combined cancer signature" was derived to differentiate either CRC and PC from controls (MDR1, SRBC, VHL, MUC2, RB1, SYK and GPC3) AUC = 0.8177, Sens = 0.6316 Spec = 0.840. In a validation cohort, N = 10 CRC patients, the optimal CRC signature (CYCD2, HIC and VHL) had AUC 0.900. In all derived signatures (CRC, PC and combined cancer signature) the optimal panel used preferential VHL methylation. In conclusion, CRC and PC differ in specific genes methylated in plasma other than VHL. Preferential methylation of VHL is shared in the optimal signature for CRC alone, PC alone and combined PC and CRC. Future investigations may identify additional methylation markers informative for the presence of both CRC and PC.

Methylation of death-associated protein kinase is associated with cetuximab and erlotinib resistance.

Anti-EGFR therapy is among the most promising molecular targeted therapies against cancer developed in the past decade. However, drug resistance eventually arises in most, if not all, treated patients. Emerging evidence has linked epigenetic changes, such as DNA methylation at CpG islands, to the development of resistance to multiple anticancer drugs. In addition, genes that are differentially methylated have increasingly been appreciated as a source of clinically relevant biomarker candidates. To identify genes that are specifically methylated during the evolution of resistance to anti-EGFR therapeutic agents, we performed a methylation-specific array containing a panel of 56 genes that are commonly known to be regulated through promoter methylation in two parental non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC) cell lines and their resistant derivatives to either erlotinib or cetuximab. We found that death-associated protein kinase (DAPK) was hypermethylated in drug-resistant derivatives generated from both parental cell lines. Restoration of DAPK into the resistant NSCLC cells by stable transfection re-sensitized the cells to both erlotinib and cetuximab. Conversely, siRNA-mediated knockdown of DAPK induced resistance in the parental sensitive cells. These results demonstrate that DAPK plays important roles in both cetuximab and erlotinib resistance, and that gene silencing through promoter methylation is one of the key mechanisms of developed resistance to anti-EGFR therapeutic agents. In conclusion, DAPK could be a novel target to overcome resistance to anti-EGFR agents to improve the therapeutic benefit, and further evaluation of DAPK methylation as a potential biomarker of drug response is needed.

DNA methylation patterns in blood of patients with colorectal cancer and adenomatous colorectal polyps.

Colorectal cancer (CRC) screening rates are currently suboptimal. Blood-based screening could improve rates of earlier detection for CRC and adenomatous colorectal polyps. In this study, we evaluated the feasibility of plasma-based detection of early CRC and adenomatous polyps using array-mediated analysis methylation profiling of 56 genes implicated in carcinogenesis. Methylation of 56 genes in patients with Stages I and II CRC (N=30) and those with adenomatous polyps (N=30) were compared with individuals who underwent colonoscopy and were found to have neither adenomatous changes nor CRC. Composite biomarkers were developed for adenomatous polyps and CRC, and their sensitivity and specificity was estimated using five-fold cross validation. Six promoters (CYCD2, HIC1, PAX 5, RASSF1A, RB1 and SRBC) were selected for the biomarker, which differentiated CRC patients and controls with 84% sensitivity and 68% specificity. Three promoters (HIC1, MDG1 and RASSF1A) were selected for the biomarker, which differentiated patients with adenomatous polyps and controls with sensitivity of 55% and specificity of 65%. Methylation profiling of plasma DNA can detect early CRC with significant accuracy and shows promise as a methodology to develop biomarkers for CRC screening.

DNA methylation as clinically useful biomarkers-light at the end of the tunnel.

A recent expansion of our knowledge about epigenetic changes strongly suggests that epigenetic rather than genetic features better reflect disease development, and consequently, can become more conclusive biomarkers for the detection and diagnosis of different diseases. In this paper we will concentrate on the current advances in DNA methylation studies that demonstrate a direct link between abnormal DNA methylation and a disease. This link can be used to develop diagnostic biomarkers that will precisely identify a particular disease. It also appears that disease-specific DNA methylation patterns undergo unique changes in response to treatment with a particular drug, thus raising the possibility of DNA methylation-based biomarkers for the monitoring of treatment efficacy, for prediction of response to treatment, and for the prognosis of outcome. While biomarkers for oncology are the most obvious applications, other fields of medicine are likely to benefit as well. This potential is demonstrated by DNA methylation-based biomarkers for neurological and psychiatric diseases. A special requirement for a biomarker is the possibility of longitudinal testing. In this regard cell-free circulating DNA from blood is especially interesting because it carries methylation markers specific for a particular disease. Although only a few DNA methylation-based biomarkers have attained clinical relevance, the ongoing efforts to decipher disease-specific methylation patterns are likely to produce additional biomarkers for detection, diagnosis, and monitoring of different diseases in the near future.

The MethDet: a technology for biomarker development.

Early detection and diagnosis of a disease in its presymptomatic form has to rely on biomarkers, and multiple laboratories are involved in their development and validation. In this article, we describe our work on a platform technology for a genome-wide analysis of DNA methylation while still using a small amount of sample - a biopsy, a section from a formalin-fixed paraffin-embedded tissue or a small volume (0.4 ml) of plasma from blood. This technology (methylation detection or MethDet) allows genome-wide association studies similar to the analysis of single-nucleotide polymorphisms. Instead of mostly static genetic differences, the MethDet technology tests disease-dependent changes of epigenetic makeup, which is closely related to the gene expression pattern of a disease. The MethDet assay has the capacity to utilize highly fragmented DNA (e.g., cell-free circulating DNA from plasma) to identify disease-specific changes, effects of treatment or changes in the disease activity.

Methylation patterns in cell-free plasma DNA reflect removal of the primary tumor and drug treatment of breast cancer patients.

Abnormal DNA methylation is a feature of most types of cancer, which is reflected in cell-free circulating DNA in plasma. It is, however, unknown whether surgical removal of the tumor and subsequent therapy induces changes in plasma DNA methylation, which can be used to monitor treatment. In this pilot study, methylation in cell-free plasma DNA of 20 breast cancer patients was determined by the previously developed MethDet-56 technique. Samples at three time points were analyzed-before surgery (baseline), after surgery (to evaluate the effects of resection) and after surgery on tamoxifen therapy (to determine the effects of treatment). Methylation patterns of healthy controls were used as a reference for all comparisons. Seven promoters were differentially methylated (p < 0.05) in at least one comparison; three changed after surgery; another one changed after beginning of tamoxifen treatment; and four were differentially methylated in baseline versus combined treatment samples. Increased methylation of PR PROX, MDGI, PAX 5 and RARß2 at baseline (presurgery) diminished toward the healthy controls with the lowest methylation in the combined treatment group. Surgery alone decreased methylation in PAX 5 and RARß2, whereas tamoxifen treatment changed methylation only in the B promoter of ESR1. Methylation patterns in cell-free plasma DNA change after surgery and tamoxifen treatment, most significantly-after combined treatment. The baseline (presurgery) patterns become similar to those of healthy controls, suggesting that methylation patterns in cell-free plasma DNA may be used to monitor treatment.

Distinctive DNA methylation patterns of cell-free plasma DNA in women with malignant ovarian tumors.

OBJECTIVE: Epithelial ovarian carcinoma (OvCa) is rarely detected early, and it is also difficult to determine whether an adnexal mass is benign or malignant. Previously, we noted differences in methylation patterns of cell-free plasma DNA (cfpDNA) in women without disease compared to patients with OvCa. In this work, we investigated whether methylation patterns of cfpDNA can differentiate between benign and malignant tumors. METHODS: Methylation patterns in cfpDNA were determined in three cohorts (30 samples each) using a microarray-based assay (MethDet 56). Principal component analysis, supervised clustering, linear discrimination analysis, and 25 rounds of 5-fold cross-validation were used to determine informative genes and assess the sensitivity and specificity of differentiating between OvCa vs. healthy control (HC), benign ovarian disease (mostly serous cystadenoma, BOD) vs. HC, and OvCa vs. BOD samples. RESULTS: Differential methylation of three promoters (RASSF1A, CALCA, and EP300) differentiated between OvCa vs. HC with a sensitivity of 90.0% and a specificity of 86.7%. Three different promoters (BRCA1, CALCA, and CDKN1C) were informative for differentiating between BOD vs. HC, with a sensitivity of 90.0% and a specificity of 76.7%. Finally, two promoters (RASSF1A and PGR-PROX) were informative for differentiating between OvCa vs. BOD, with a sensitivity of 80.0% and a specificity of 73.3%. CONCLUSIONS: This proof-of-principle data show that differential methylation of promoters in cfpDNA may be a useful biomarker to differentiate between certain benign and malignant ovarian tumors.

Differential methylation of cell-free circulating DNA among patients with pancreatic cancer versus chronic pancreatitis.

BACKGROUND: : Although patients with chronic pancreatitis (CP) have an increased risk of pancreatic cancer (PanCa), the timely detection of PanCa often is difficult, because the symptoms of CP and PanCa are very similar. Moreover, secondary inflammation may be identified in PanCa, further complicating diagnosis. To improve the survival of patients with PanCa, a reliable test to differentiate CP from PanCa is needed. In this article, the authors describe a methylation profile of cell-free plasma DNA that distinguished CP from PanCa with >90% accuracy. METHODS: : Methylation in cell-free, plasma DNA was compared among 30 samples from patients with CP, 30 samples from patients with PanCa, and 30 samples from healthy controls (N) using a microarray-mediated methylation analysis of 56 fragments in each sample (MethDet56). Statistical analysis was done by using the Fisher exact test, a naive Bayes algorithm, and 25 rounds of 5-fold cross-validation. RESULTS: : The MethDet56 methylation analysis technique identified 17 gene promoters as informative (8 for distinguishing N from CP and 14 for distinguishing CP from PanCa). It achieved 81.7% sensitivity and 78% specificity (P<.01) in the detection of CP (N vs CP) and 91.2% sensitivity and 90.8% specificity (P<.01) in the differential detection of PanCa (PanCa vs CP). CONCLUSIONS: : The current data suggested that, among patients with pancreatic disease, the methylation profiles of inflammatory disease and cancer are different and open a new venue for the development of biomarkers for differential diagnosis. Further investigation of diagnostic biomarkers for pancreatic cancer based on methylation in cell-free, circulating DNA appears to be warranted. Cancer 2010. (c) 2010 American Cancer Society.

Methylation patterns of cell-free plasma DNA in relapsing-remitting multiple sclerosis.

BACKGROUND: There is growing interest for identification of new targets for biomarker development in multiple sclerosis (MS). The goal of this study was to compare the concentration and the methylation patterns of cell-free plasma DNA (cfpDNA) in patients with relapsing-remitting multiple sclerosis (RRMS) and healthy individuals. METHODS: Three 30-patient cohorts were examined: patients with RRMS, in either remission or exacerbation, and healthy individuals as controls. Concentration of cfpDNA was determined using a standard fluorometric assay. Patterns of methylation in 56 gene promoters were determined by a microarray-based assay (MethDet-56). The data were analyzed to identify statistically relevant differences among the study groups. RESULTS: The concentration of cfpDNA in patients with RRMS was four to eight-fold higher compared to healthy controls. Significant differences in cfpDNA methylation patterns were detected in all three comparisons: RRMS patients in remission versus healthy controls were recognized with 79.2% sensitivity and 92.9% specificity; RRMS patients in exacerbation versus healthy controls were recognized with 75.9% sensitivity and 91.5% specificity; and RRMS patients in exacerbation versus those in remission were recognized with 70.8% sensitivity and 71.2% specificity. CONCLUSION: Based on our findings, we conclude that patients with RRMS display unique disease- and state-specific changes of cfpDNA. Our findings are of clinical significance as they could be used in the development of potentially new biomarkers for MS. This is the first report in our knowledge describing such changes of cfpDNA in patients with MS.

Differential methylation profile of ovarian cancer in tissues and plasma.

An accurate biomarker for detection of ovarian cancer may reduce cancer-related mortality. Using a previously developed microarray-based technique, we evaluated differences in DNA methylation profiles in a panel of 56 genes using sections of serous papillary adenocarcinomas and uninvolved ovaries (n=30) from women in a high-risk group. Methylation profiles were also generated for circulating DNA from blood of patients (n=33) and healthy controls (n=33). Using the most differentially methylated genes for naïve Bayesian analysis, we identified ten of these profiles as potentially informative in tissues. Various combinations of these genes produced 69% sensitivity and 70% specificity for cancer detection as estimated under a stratified, fivefold cross-validation protocol. In plasma, five genes were identified as informative; their combination had 85% sensitivity and 61% specificity for cancer detection. These results suggest that differential methylation profiling in heterogeneous samples has the potential to identify components of a composite biomarker that may detect ovarian cancer in blood with significant accuracy.

Methylation profile of circulating plasma DNA in patients with pancreatic cancer.

BACKGROUND AND OBJECTIVES: Detection of pancreatic cancer by blood-based test may improve outcomes. We sought to establish the feasibility of a blood-based detection of pancreatic cancer through multiplexed array-mediated analysis of DNA methylation. METHODS: Methylation was assessed in each plasma sample using a panel of 56 frequently methylated genes. Methylation profiles in patients with ductal cell adenocarcinoma of the pancreas (n = 30) and healthy gender and age-matched controls (n = 30) were compared. Methylation was determined as described previously; a composite biomarker was developed for classification of cancer and normal samples. Sensitivity and specificity of the biomarker were estimated using 25 rounds of fivefold cross-validation. RESULTS: Five promoters were consistently selected for the classifier during cross-validation and comprised the final composite biomarker Five-fold cross-validation results indicate 76% sensitivity and 59% specificity of the biomarker, which included promoters of CCND2, SOCS1, THBS1, PLAU, and VHL. CONCLUSION: Differential methylation profiling of plasma DNA can detect ductal adenocarcinoma of the pancreas with significant accuracy and should be explored further. While additional improvement of biomarkers is necessary, the blood-based biomarker may be already useful as a first-line detection tool.

Array-based multiplex analysis of DNA methylation in breast cancer tissues.

Abnormal DNA methylation is well established for cancer cells, but a methylation-based diagnostic test is yet to be developed. One of the problems is insufficient accuracy of cancer detection in heterogeneous clinical specimens when only a single gene is analyzed. A new technique was developed to produce a multigene methylation signature in each sample, and its potential for selection of informative genes was tested using DNA from formalin-fixed, paraffin-embedded breast cancer tissues. Fifty-six promoters were analyzed in each of 138 clinical specimens by a microarray-based modification of the previously developed technique. Specific methylation signatures were identified for atypical ductal hyperplasia, ductal carcinoma in situ, and invasive ductal carcinoma. Informative promoters selected by Fisher's exact test were used for composite biomarker design using naïve Bayes algorithm. All informative promoters were unmethylated in disease compared with normal tissue. Cross-validation showed 72.4% sensitivity and 74.7% specificity for detection of ductal carcinoma in situ and invasive ductal carcinoma, and 87.5% sensitivity and 95% specificity for detection of atypical ductal hyperplasia. These results indicate that informative cancer-specific methylation signatures can be detected in heterogeneous tissue specimens, suggesting that a diagnostic assay can then be developed.

MSRE-PCR for analysis of gene-specific DNA methylation.

Abnormal DNA methylation is observed in certain promoters of neoplastic cells, although the likelihood of methylation for each individual promoter varies. Simultaneous analysis of many promoters in the same sample can allow use of statistical methods for identification of neoplasia. Here we describe an assay for such analysis, based on digestion of genomic DNA with methylation-sensitive restriction enzyme and multiplexed PCR with gene-specific primers (MSRE-PCR). MSRE-PCR includes extensive digestion of genomic DNA (uncut fragments cannot be identified by PCR), can be applied to dilute samples (<1 pg/microl), requires limited amount of starting material (42 pg or genomic equivalent of seven cells) and can identify methylation in a heterogeneous mix containing <2% of cells with methylated fragments. When applied to 53 promoters of breast cancer cell lines MCF-7, MDA-MB-231 and T47D, MSRE-PCR correctly identified the methylation status of genes analyzed by other techniques. For selected genes results of MSRE-PCR were confirmed by methylation-specific PCR and bisulfite sequencing. The assay can be configured for any number of desired targets in any user-defined set of genes.