Integrative analysis of complex genomic and epigenomic maps

Modern healthcare research demands collaboration across disciplines to build preventive measures and innovate predictive capabilities for curing diseases. Along with the emergence of cutting-edge computational and statistical methodologies, data generation and analysis has become cheaper in the last ten years. However, the complexity of big data due to its variety, volume, and velocity creates new challenges for biologists, physicians, bioinformaticians, statisticians, and computer scientists. Combining data from complex multiple profiles is useful to better understand cellular functions and pathways that regulates cell function to provide insights that could not have been obtained using the individual profiles alone. However, current normalization and artifact correction methods are platform and data type specific, and may require both the training and test sets for any application (e.g. biomarker development). This often leads to over-fitting and reduces the reproducibility of genomic findings across studies. In addition, many bias correction and integration approaches require renormalization or reanalysis if additional samples are later introduced. The motivation behind this research was to develop and evaluate strategies for addressing data integration issues across data types and profiling platforms, which should improve healthcare-informatics research and its application in personalized medicine. We have demonstrated a comprehensive and coordinated framework for data standardization across tissue types and profiling platforms. This allows easy integration of data from multiple data generating consortiums. The main goal of this research was to identify regions of genetic-epigenetic co-ordination that are independent of tissue type and consistent across epigenomics profiling data platforms. We developed multi-‘omic’ therapeutic biomarkers for epigenetic drug efficacy by combining our biomarker regions with drug perturbation data generated in our previous studies. We used an adaptive Bayesian factor analysis approach to develop biomarkers for multiple HDACs simultaneously, allowing for predictions of comparative efficacy between the drugs. We showed that this approach leads to different predictions across breast cancer subtypes compared to profiling the drugs separately. We extended this approach on patient samples from multiple public data resources containing epigenetic profiling data from cancer and normal tissues (The Cancer Genome Atlas, TCGA; NIH Roadmap epigenomics data)

Epigenetic Biomarkers for Environmental Exposures and Personalized Breast Cancer Prevention.

Environmental and lifestyle factors are believed to account for >80% of breast cancers; however, it is not well understood how and when these factors affect risk and which exposed individuals will actually develop the disease. While alcohol consumption, obesity, and hormone therapy are some known risk factors for breast cancer, other exposures associated with breast cancer risk have not yet been identified or well characterized. In this paper, it is proposed that the identification of blood epigenetic markers for personal, in utero, and ancestral environmental exposures can help researchers better understand known and potential relationships between exposures and breast cancer risk and may enable personalized prevention strategies

Translational Oncogenomics and Human Cancer Interactome Networks

An overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes

Precision medicine based in epigenomics: the paradigm of carcinoma of unknown primary

Epigenetic alterations are a common hallmark of human cancer. Single epigenetic markers are starting to be incorporated into clinical practice; however, the translational use of these biomarkers has not been validated at the 'omics' level. The identification of the tissue of origin in patients with cancer of unknown primary (CUP) is an example of how epigenomics can be incorporated in clinical settings, addressing an unmet need in the diagnostic and clinical management of these patients. Despite the great diagnostic advances made in the past decade, the use of traditional diagnostic procedures only enables the tissue of origin to be determined in ∼30% of patients with CUP. Thus, development of molecularly guided diagnostic strategies has emerged to complement traditional procedures, thereby improving the clinical management of patients with CUP. In this Review, we present the latest data on strategies using epigenetics and other molecular biomarkers to guide therapeutic decisions involving patients with CUP, and we highlight areas warranting further research to engage the medical community in this unmet need

Epigenomics in cancer management

The identification of all epigenetic modifications implicated in gene expression is the next step for a better understanding of human biology in both normal and pathological states. This field is referred to as epigenomics, and it is defined as epigenetic changes (ie, DNA methylation, histone modifications and regulation by noncoding RNAs such as microRNAs) on a genomic scale rather than a single gene. Epigenetics modulate the structure of the chromatin, thereby affecting the transcription of genes in the genome. Different studies have already identified changes in epigenetic modifications in a few genes in specific pathways in cancers. Based on these epigenetic changes, drugs against different types of tumors were developed, which mainly target epimutations in the genome. Examples include DNA methylation inhibitors, histone modification inhibitors, and small molecules that target chromatin-remodeling proteins. However, these drugs are not specific, and side effects are a major problem; therefore, new DNA sequencing technologies combined with epigenomic tools have the potential to identify novel biomarkers and better molecular targets to treat cancers. The purpose of this review is to discuss current and emerging epigenomic tools and to address how these new technologies may impact the future of cancer management

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

Epigenetic treatment of solid tumours. A review of clinical trials

Epigenetic treatment has been approved by regulatory agencies for haematological malignancies. The success observe in cutaneous lymphomas represents a proof of principle that similar results may be obtained in solid tumours. Several agents that interfere with DNA methylation-demethylation and histones acetylation/deacetylation have been studied, and some (such as azacytidine, decitabine, valproic acid and vorinostat) are already in clinical use. The aim of this review is to provide a brief overview of the molecular events underlying the antitumour effects of epigenetic treatments and to summarise data available on clinical trials that tested the use of epigenetic agents against solid tumours. We not only list results but also try to indicate how the proper evaluation of this treatment might result in a better selection of effective agents and in a more rapid development. We divided compounds in demethylating agents and HDAC inhibitors. For each class, we report the antitumour activity and the toxic side effects. When available, we describe plasma pharmacokinetics and pharmacodynamic evaluation in tumours and in surrogate tissues (generally white blood cells). Epigenetic treatment is a reality in haematological malignancies and deserves adequate attention in solid tumours. A careful consideration of available clinical data however is required for faster drug development and possibly to re-evaluate some molecules that were perhaps discarded too early