Epigenetics Of Cancer
The ‘Epigenetic Fingerprint’ of Human Mammary Cancer: A Genome-Wide Tool for Precision Diagnostics
Most genetic variants that confer a risk to inherited diseases are found in regions of the genome that do not code for any characterized protein. Distinguishing the functional elements affected by these genetic variants from the large stretches of non-functional DNA which surrounds them remains an extremely challenging task, yet one that will have a fundamental impact on the biomedical sciences. My studies will create a scalable new technology that maps the genomic location and activity of functional elements, including promoters, enhancers, lincRNAs and protein-coding genes. The central innovation proposed here is a combination of machine-learning algorithms to detect ‘shapes’ that are characteristic of distinct functional compartments that together make up the epigenome. My approach, called EPI-MAP, works in combination with data from a single genome-wide molecular assay (PRO-seq), which can be rapidly and inexpensively applied to primary tissue samples. Thanks to this efficiency, the proposed technology is uniquely suited to scaling-up with a potential for diverse applications in research and clinical medicine.
One disease whose understanding would be transformed by large-scale ‘epigenetic’ mapping is the study of breast cancer. Breast cancer is fundamentally a disease of gene expression, and many of the genetic variants which confer a risk to breast cancer are located in non-coding regions of the genome. Although changes in gene expression have been widely studied in primary tumors, most changes in gene expression are passively responding to the tumor’s environment. In contrast, the regulatory code which controls gene expression is directly responsible for actively driving expression programs that promote tumor growth. The central hypothesis of the proposed work is that the epigenetic code actively controls the growth and progression of all subtypes of breast tumors, and that systematically mapping this code will reveal the molecular pathways that are driving growth in neoplastic cells.