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Epigenetic deregulation has emerged as a driver in human malignancies. There is no clear understanding of the epigenetic alterations in hepatocellular carcinoma (HCC) and of the potential role of DNA methylation markers as prognostic biomarkers. Analysis of tumor tissue from 304 patients with HCC treated with surgical resection allowed us to generate a methylation-based prognostic signature using a training-validation scheme. Methylome profiling was done with the Illumina HumanMethylation450 array (Illumina, Inc., San Diego, CA), which covers 96% of known cytosine-phosphate-guanine (CpG) islands and 485,000 CpG, and transcriptome profiling was performed with Affymetrix Human Genome U219 Plate (Affymetrix, Inc., Santa Clara, CA) and miRNA Chip 2.0. Random survival forests enabled us to generate a methylation signature based on 36 methylation probes. We computed a risk score of mortality for each individual that accurately discriminated patient survival both in the training (221 patients; 47% hepatitis C-related HCC) and validation sets (n = 83; 47% alcohol-related HCC). This signature correlated with known predictors of poor outcome and retained independent prognostic capacity of survival along with multinodularity and platelet count. The subset of patients identified by this signature was enriched in the molecular subclass of proliferation with progenitor cell features. The study confirmed a high prevalence of genes known to be deregulated by aberrant methylation in HCC (e.g., Ras association [RalGDS/AF-6] domain family member 1, insulin-like growth factor 2, and adenomatous polyposis coli) and other solid tumors (e.g., NOTCH3) and describes potential candidate epidrivers (e.g., septin 9 and ephrin B2). Conclusions: A validated signature of 36 DNA methylation markers accurately predicts poor survival in patients with HCC. Patients with this methylation profile harbor messenger RNA-based signatures indicating tumors with progenitor cell features.

Purpose: Lung cancer remains as the leading cause of cancer-related death worldwide, mainly due to late diagnosis. Cytology is the gold-standard method for lung cancer diagnosis in minimally invasive respiratory samples, despite its low sensitivity. We aimed to identify epigenetic biomarkers with clinical utility for cancer diagnosis in minimally/noninvasive specimens to improve accuracy of current technologies. Experimental Design: The identification of novel epigenetic biomarkers in stage I lung tumors was accomplished using an integrative genome-wide restrictive analysis of two different large public databases. DNA methylation levels for the selected biomarkers were validated by pyrosequencing in paraffin-embedded tissues and minimally invasive and noninvasive respiratory samples in independent cohorts. Results: We identified nine cancer-specific hypermethylated genes in early-stage lung primary tumors. Four of these genes presented consistent CpG island hypermethylation compared with nonmalignant lung and were associated with transcriptional silencing. A diagnostic signature was built using multivariate logistic regression model based on the combination of four genes: BCAT1, CDO1, TRIM58, and ZNF177. Clinical diagnostic value was also validated in multiple independent cohorts and yielded a remarkable diagnostic accuracy in all cohorts tested. Calibrated and cross-validated epigenetic model predicts with high accuracy the probability to detect cancer in minimally and noninvasive samples. We demonstrated that this epigenetic signature achieved higher diagnostic efficacy in bronchial fluids as compared with conventional cytology for lung cancer diagnosis. Conclusions: Minimally invasive epigenetic biomarkers have emerged as promising tools for cancer diagnosis. The herein obtained epigenetic model in combination with current diagnostic protocols may improve early diagnosis and outcome of lung cancer patients. Clin Cancer Res; 22(13); 3361–71. ©2016 AACR.

Epigenetic factors (DNA methylation, histone modifications, or ncRNAs) are involved in gene expression regulation. Thus, determining abnormal epigenetic changes is a suitable approach to extract meaningful information about human diseases. An altered pattern of epigenetic modifications has been firstly defined as a hallmark for cancer, although it is also a key element to many common human diseases, such as cardiovascular, metabolic, and neurological pathologies. During the last decade, the advent of genome-scale analysis techniques applied to epigenetics has provided a massive amount of data, enabling an important advance in the molecular mechanisms underlying disease initiation, progression, and expansion. Disease-specific epigenomic signatures, mainly based on DNA methylation analysis, have been studied for several clinical purposes including prognostics and diagnostics, as well as disease-specific chemotherapy response. Using noninvasive specimens, epigenetic profiling holds the promise of being of clinical value in the management of patients, even at the early stages of disease. Additionally, epigenetic marks have also been catalogued as targets for pharmacological drugs. The upgrade of epigenetic research to epigenomics together with other –omics would tackle the many unanswered questions in the field, paving the path to achieve a more precise personalized medicine.

Purpose Non–small-cell lung cancer (NSCLC) is a tumor in which only small improvements in clinical outcome have been achieved. The issue is critical for stage I patients for whom there are no available biomarkers that indicate which high-risk patients should receive adjuvant chemotherapy. We aimed to find DNA methylation markers that could be helpful in this regard. Patients and Methods A DNA methylation microarray that analyzes 450,000 CpG sites was used to study tumoral DNA obtained from 444 patients with NSCLC that included 237 stage I tumors. The prognostic DNA methylation markers were validated by a single-methylation pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC. Results Unsupervised clustering of the 10,000 most variable DNA methylation sites in the discovery cohort identified patients with high-risk stage I NSCLC who had shorter relapse-free survival (RFS; hazard ratio [HR], 2.35; 95% CI, 1.29 to 4.28; P = .004). The study in the validation cohort of the significant methylated sites from the discovery cohort found that hypermethylation of five genes was significantly associated with shorter RFS in stage I NSCLC: HIST1H4F, PCDHGB6, NPBWR1, ALX1, and HOXA9. A signature based on the number of hypermethylated events distinguished patients with high- and low-risk stage I NSCLC (HR, 3.24; 95% CI, 1.61 to 6.54; P = .001). Conclusion The DNA methylation signature of NSCLC affects the outcome of stage I patients, and it can be practically determined by user-friendly polymerase chain reaction assays. The analysis of the best DNA methylation biomarkers improved prognostic accuracy beyond standard staging.

Personalized medicine is defined by therapy decisions tailored to individual patients, aiming to improve therapeutic efficiencies and to minimize side effects. The current clinical practice includes targeted therapies for disease-related alterations and molecular biomarker-based patient stratification. However, recent advances in screening technologies have enabled more comprehensive identification strategies and suggest a plethora of additional valuable biomarkers and druggable molecules for future clinical applications. Beside genetic alterations, in particular, DNA methylation biomarkers emerge into the field by presenting stable DNA modifications with predictive potential for drug treatment efficiencies, especially in a cancer context. Although not directly affecting the genetic code, DNA methylation exhibits regulatory functions with high impact on disease onset and progression. In this article, the authors summarize the current knowledge of DNA methylation biomarkers for treatment efficiencies and evaluate their translational value into clinical use.

Introduction: Lung cancer is the leading cause worldwide, mainly due to late diagnosis. The aim of this work was to identify a panel of epigenetic biomarkers for improving early diagnosis of lung cancer patients using minimally and non-invasive biological fluids. Patients and Methods: DNA hypermethylated biomarkers were identified performing a Genome-wide DNA methylation analysis (Infinium 450K array) in Non-small cell lung cancer (NSCLC) primary tumors from two different public databases (Discovery cohorts): CURELUNG FP7 Consortium (237 stage I NSCLC, 25 non-tumoral lung samples) and The Cancer Genome Atlas (TCGA; 350 stage I NSCLC, 62 non-tumoral lung samples). DNA methylation levels of selected candidates were analyzed by pyrosequencing in non- or minimally invasive samples from three independent cohorts of stage I NSCLC patients and non-tumoral controls (Validation cohorts): bronchoalveolar aspirates (82 NSCLC; 29 controls), bronchoalveolar lavages (51 NSCLC; 29 controls) and sputum (72 NSCLC; 26 controls). Combined Receiver Operating Characteristic (ROC) curve was obtained to evaluate the diagnostic utility of the epigenetic signature. Results: We identified a panel of 4 cancer-specific genes (BCAT1, CDO1, TRIM58 and ZNF177) with CpG island hypermethylation-associated silencing in early stage NSCLC primary tumors. All these genes presented significantly higher mean levels of%methylation (M) in NSCLC primary tumors respect to non-tumoral controls: BCAT1 (NSCLC: M>50%; Controls: M40%; Controls: M50%; Controls: M40%; Controls : M Conclusions: The herein identified DNA methylation signature could improve, in combination with current diagnostic protocols, the early diagnosis and outcome of NSCLC patients. The high diagnostic accuracy of this signature obtained in liquid biopsy offers a minimally invasive and easy accessible tool for early lung cancer diagnosis. Citation Format: A Diaz-Lagares, J Mendez-Gonzalez, D Hervas, M Saigi, MJ Pajares, D Garcia, AB Crujeiras, R Lopez-Lopez, R Pio, LM Montuenga, JJ Zulueta, E Nadal, A Rosell, M Esteller, J Sandoval. Identification of a DNA methylation signature in liquid biopsy for early non-small cell lung cancer (NSCLC) diagnosis. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-155.

Although the five-year survival of childhood acute lymphoblastic leukaemia (ALL) exceeds 80%, a group of patients presents poor prognosis due to early relapse (van den Berg et al, 2011). To date, treatment strategies have been defined by cytogenetically-based subtype categorization. However, ALL patients without chromosomal translocations associated with poor prognosis lack diagnostic markers that would allow specific therapies to be developed. DNA methylation alteration is a frequent event in cancer and is potentially very useful in the diagnosis, prognosis and prediction of drug response (Rodriguez-Paredes & Esteller, 2011). Hence, we attempted to characterize childhood B-ALLs without Philadelphia (BCR-ABL1) and MLL translocations on the basis of the DNA methylation profile of more than 450 000 CpG sites with the aim of providing a means to improve the accuracy of prognosis and treatment strategies. All the obtained DNA methylation data have been deposited in the Gene Expression Omnibus (GEO) database in the following link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=bfsbfcigsakcuty&acc={"type":"entrez-geo","attrs":{"text":"GSE39141","term_id":"39141"}}GSE39141 We derived genome-wide DNA methylation profiles of 29 childhood B-ALL patients and four normal B-cell samples (NBC) using the Infinium 450 K DNA methylation Bead assay (450 K) (Deneberg et al, 2011). Twenty-five patient samples were obtained at the time of diagnosis (including 10 cytogenetically normal, eight hyperdiploid, five pseudodiploid and two unclassified samples) and four samples at disease relapse (Table 1). Profiling varying 11 112 CpG sites (SD>0·25) within all samples analysed clearly distinguished healthy B-cell specimens from B-ALL patient samples (Fig 1A). Figure 1 Genome-wide DNA methylation profile of B-cell ALL patients. (A) Unsupervised hierarchical clustering of four normal B-cell donors (yellow) and 29 ALL patients (orange) using CpGs with standard deviation>0.25. The cytogenetic subtypes and disease ... Table 1 Clinical characteristics of the B-ALL samples Overall, B-ALL samples had a significantly greater variance in DNA methylation level than NBC samples (Wilcoxon test, P 0·05). However, the highest variance (SD≥0·25) was DNA hypermethylation found within the CGI context (Fig 1C, Fig S1), consistent with previous results obtained with lower coverage platforms (Milani et al, 2010). To obtain closer insight into the nature of variant sites, we determined differentially methylated CpG sites (dmCpGs) between healthy and cancer specimens (Table S1). We identified 3,414 dmCpGs, 88·2% (3,014) and 11·8% (400) of which respectively lost and gained DNA methylation in cancer samples (Fig 1D; Fig S2). Despite the predominantly hypomethylated dmCpGs, CGIs in gene promoters significantly gained DNA methylation at dmCpGs (Wilcoxon test; P < 0·01). Interestingly, the CGIs flanking CpG-poor regions (CpG island shores) were hypomethylated in ALL samples (Wilcoxon test; P < 0·01), consistent with previous studies identifying both regions as being highly variable in different cancer types, including leukaemia (Milani et al, 2010; Irizarry et al, 2009). Analysing variant CpG sites in an unsupervised manner, we identified two clearly distinct DNA methylome profiles in B-ALL patients (Fig 1A). While 14 samples (ALL-1) displayed highly aberrant methylation levels compared with the control, 15 samples (ALL-2) showed close similarities to healthy B-cells. Most strikingly, the five with disease-relapse-associated samples were all present in ALL-2 (5/15; X2 test, P < 0·01), presenting a signature with a significant association between DNA methylation and disease-free survival (log-rank Mantel-Cox test, P < 0·01; Fig 1E) and suggesting a possible application in future therapy strategies by taking into account epigenetically defined groups as previously suggested (Milani et al, 2010). Considering the presence of distinct DNA methylation subtypes in childhood B-ALLs and their potential application in clinical practice, we extracted a DNA methylation profile represented by 20 661 dmCpGs that distinguished the two groups (Table S2). In total, we detected 17 182 hypo- and 3 479 hypermethylated CpG sites in ALL-2 compared with ALL-1, respectively; differing in variance and associated with unique genomic features (Fig 1F-H). Confirming the signature in a 10-fold cross-validation model (area under the curve: 89·5), we concluded that the signature reliably detected both B-ALL subtypes and is thus an important tool for future disease diagnosis. In order to determine the affected biological and disease-associated pathways, we analysed the gene ontology (GO) of hyper- and hypomethylated CpG sites located in gene promoters. We noticed an enrichment (GO, level 5) of genes related to lymphocyte (including B-cell) differentiation in 672 gene promoters presenting higher methylation level in ALL-2 (false discovery rate [FDR]