Interrogating Epigenetic Changes in Cancer Genomes

  • Nephew, Kenneth (PI)
  • Saltz, Joel (PI)
  • Friedman, Avner (PI)
  • Davuluri, Ramana (PI)
  • Charis, Eng (PI)
  • Huang, Tim H.-M. (PI)

Project: Research project

Project Details

Description

DESCRIPTION (provided by applicant): The investigators of the Ohio State University submit a center grant application in response to the RFA announcement of the Integrated Cancer Biology Program (ICBP). In addition, our colleagues at the neighboring Indiana University will join this endeavor. The proposed research will tackle a unique problem, i.e., epigenetic alteration, now being considered as important as genetic mutations in cancer. This phenomenon can be defined as a heritable change that modulates chromatin organization and gene expression without altering nucleotide sequences. Bringing together experimental and computational biologists, our overall goals of this ICBP are 1) to increase our understanding of complex epigenetic interactions in neoplasms and 2) to use high-end information for improved prognosis, intervention, and treatment of human female cancers. Experimental biologists will use novel microarray platforms to interrogate DNA methylation, histone modifications, loss of heterozygosity, and transcription factor binding in cancer cell lines and neoplastic epithelium and the surrounding stroma. Computational biologists will use these experimental data for model building and refinement. Empirical Bayesian models will be used to predict how repressors, histone deacetylases, and DMA methyltransferases are recruited to establish epigenetic gene silencing (Project 1). Phylogenetic clustering algorithms will be developed to recapitulate genetic and epigenetic pathways in cancer stroma as they relate to tumor progression (Project 2). LASSO logistic regression and neural network approaches will be used to model the synergistic DNA-protein interactions and the resulting change of chromatin landscape in cancer cells (Project 3). Pattern recognition and supervised learning techniques will be used to select genes that contain the characteristics of methylation-prone sequences in drug-resistant cancer cells (Project 4). These mathematical models will generate the first- or second-level hypotheses for experimental testing. The iterative process of model refinement and experimental verification will continue until models are derived that accurately predict specific epigenetic alterations in the interrogating cancer genome. All experimental data and modeling tools will be deposited in a centralized database and will be used for training future systems cancer biologists. The progress of these integrated studies will be evaluated by advisors and administrative leaders to ensure the success of the proposed ICBP.
StatusFinished
Effective start/end date9/30/042/29/16

Funding

  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health

ASJC

  • Medicine(all)

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