Absence of an embryonic stem cell DNA methylation signature in human cancer.

BackgroundDifferentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues.MethodsWe applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of < 0.05 were considered statistically significant.ResultsAcross 20 different tumor types we observed a consistently lower FCO signature in tumor tissues compared with nontumor normal tissues, with 18 observed to have significantly lower FCO fractions in tumor tissue (total n = 6,795 tumor, n = 922 nontumor, P < 0.05). We replicated our findings in 15 tumor types using data from independent subjects in 15 publicly available data sets (total n = 740 tumor, n = 424 nontumor, P < 0.05).ConclusionsThe results suggest that cancer development itself is substantially devoid of recapitulation of normal embryologic processes. Our results emphasize the distinction between DNA methylation in normal tightly regulated stem cell driven differentiation and cancer stem cell reprogramming that involves altered methylation in the service of great cell heterogeneity and plasticity

Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection. Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TCGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients. Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients. Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods

Epigenetics in diagnosis, prognostic assessment and treatment of cancer:An update

Cancer cells contain multiple genetic and epigenetic changes. The relative specificity of many epigenetic changes for neoplastic cells has allowed the identification of diagnostic, prognostic and predictive biomarkers for a number of solid tumors and hematological malignancies. Moreover, epigenetically-acting drugs are already in routine use for cancer and numerous additional agents are in clinical trials. Here, we review recent progress in the development and application of epigenetic strategies for the diagnosis, risk stratification and treatment of cancer

Diagnostic, Prognostic and Therapeutic Value of Gene Signatures

Gene expression studies have revealed diagnostic profiles and upregulation of specific pathways in many solid tumors. Some gene-expression signatures are already used as predictors of relapse in early breast cancer patients. The explosion of new information in gene expression profiling could potentially lead to the development of tailored treatments in many solid tumors. In addition, many studies are ongoing to validate these signatures also in predicting response to hormonal, chemotherapeutic, and targeted agents in breast cancer as well as in other tumors. This book has been carried out with the aim of providing readers a useful and comprehensive resource about the range of applications of microarray technology on oncological diseases. The book is principally addressed to resident and fellow physicians, medical oncologists, molecular biologists, biotechnologists, and those who study oncological diseases. The chapters have been written by leading international researchers on these topics who have prepared their manuscripts according to current literature and field experience with microarray technology

Molecular landscape of esophageal cancer: implications for early detection and personalized therapy

Esophageal cancer (EC) is one of the most lethal cancers and a public health concern worldwide, owing to late diagnosis and lack of efficient treatment. Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are main histopathological subtypes of EC that show striking differences in geographical distribution, possibly due to differences in exposure to risk factors and lifestyles. ESCC and EAC are distinct diseases in terms of cell of origin, epidemiology, and molecular architecture of tumor cells. Past efforts aimed at translating potential molecular candidates into clinical practice proved to be challenging, underscoring the need for identifying novel candidates for early diagnosis and therapy of EC. Several major international efforts have brought about important advances in identifying molecular landscapes of ESCC and EAC toward understanding molecular mechanisms and critical molecular events driving the progression and pathological features of the disease. In our review, we summarize recent advances in the areas of genomics and epigenomics of ESCC and EAC, their mutational signatures and immunotherapy. We also discuss implications of recent advances in characterizing the genome and epigenome of EC for the discovery of diagnostic/prognostic biomarkers and development of new targets for personalized treatment and prevention

Individualized analysis reveals CpG sites with methylation aberrations in almost all lung adenocarcinoma tissues

Additional file 1: Table S1. Stable and reversal CpG site pairs identified in the samples measured by two platforms

Cell-Free DNA Methylation: The New Frontiers of Pancreatic Cancer Biomarkers' Discovery

Pancreatic ductal adenocarcinoma (PDAC) is among the most lethal cancer types world-wide. Its high mortality is related to the difficulty in the diagnosis, which often occurs when the disease is already advanced. As of today, no early diagnostic tests are available, while only a limited number of prognostic tests have reached clinical practice. The main reason is the lack of reliable biomarkers that are able to capture the early development or the progression of the disease. Hence, the discovery of biomarkers for early diagnosis or prognosis of PDAC remains, de facto, an unmet need. An increasing number of studies has shown that cell-free DNA (cfDNA) methylation analysis represents a promising non-invasive approach for the discovery of biomarkers with diagnostic or prognostic potential. In particular, cfDNA methylation could be utilized for the identification of disease-specific signatures in pre-neoplastic lesions or chronic pancreatitis (CP), representing a sensitive and non-invasive method of early diagnosis of PDAC. In this review, we will discuss the advantages and pitfalls of cfDNA methylation studies. Further, we will present the current advances in the discovery of pancreatic cancer biomarkers with early diagnostic or prognostic potential, focusing on pancreas-specific (e.g., CUX2 or REG1A) or abnormal (e.g., ADAMTS1 or BNC1) cfDNA methylation signatures in high risk pre-neoplastic conditions and PDAC

Multi-omics molecular profiling of lung tumours

Lung Cancer (LC) is one of the most common malignancies and is the leading cause of cancer death worldwide among both men and women. Current LC classifications are based on histopathological features which poorly reflect the molecular diversity of these tumours. Consequently, primary and secondary drug resistance are very frequent, and a high mortality is usual in LC patients. Despite the fact that LC has been intensively studied, there is a lack of effective biomarkers for early detection, stratification and prognosis. Integration of omics data is a powerful approach that can be used to identify molecular subgroups relevant in the clinical setting. This thesis addresses this challenge by characterising the molecular alterations accompanying LC at the genetic and DNA methylation level, using a combination of Whole-Exome Sequencing (WES), Targeted Capture Sequencing (TCS), Single Nucleotide Polymorphism (SNP) genotyping, Whole-Genome Bisulfite Sequencing and RNA-sequencing. The integration of different types of omics data first validated previous molecular alterations in frequently diagnosed LC tumours. This allowed comparison of the genomic and epigenomic landscapes between these common and rarer LC subtypes. Next, novel molecular subgroups of Non-Small Cell Lung Cancer (NSCLC) tumours with bad prognostic, as well as subgroups of Lung Carcinoids (L-CDs, an understudied LC subtype) have been identified and their molecular alterations and signatures characterised. Significant associations with histological features and gene expression programmes have been found by using several bioinformatic tools. These results show the value of multi-omics approaches to better understand the molecular mechanisms underlying LC and to identify new biomarkers. Importantly, some of these findings may be translatable and are likely to improve the detection, monitoring and stratification for targeted therapies in LC patients.Open Acces