Cancer Biology & Epigenomics
The scientific objectives of the program include the identification of molecular mechanisms underlying tumorigenesis, metastasis and development of drug resistance.
The program is strengthened by its members’ multidisciplinary expertise in clinical, translational and basic cancer research.
The program conducts translational research using high powered computing, clinical-laboratory-industry collaborations and basic bench science. Recent innovations include the elucidation of pathways and conditions that transform normal cells into malignant cells; the development of software that aids in the discovery of associations among genetics, environment and brain tumors; and the uncovering of molecular patterns associated with cancer.
Human-induced pluripotent stem cells show cancer-related markers. The Hendrix and Soares laboratories, with Vasil Galat, PhD showed that cancer hallmarks are expressed by hiPSCs. These findings will require further elucidation for their impact on clinical applications, especially with respect to the fate of precancerous stem cells.
Potassium iodide is made available to Japanese children. Berg Biosystems, a biopharma firm and the non-profit Center for Applied Innovation (CAI) accelerated the transfer of potassium iodide to Japan in an effort to protect Japanese children and nursing mothers in the wake of the March 2011 nuclear disaster. Mary J.C. Hendrix, a CAI Fellow, served as a technology advisor to the transaction.
A new resource for brain tumor data facilitates discovery. Scattered in multiple databases and supplementary materials, these data are difficult to retrieve, evaluate, compare and visualize. The Soares laboratory developed and implemented an open access database (BTECH) for the deposition of molecular data from brain tumor studies. This integrated platform facilitates uncovering interactions among genetic and epigenetic factors.
Methylation entropy to decode epigenetic data. Abnormal DNA methylation is commonly observed in cancer and other diseases. The conventional methodology used to detect methylation cannot dissect methylation patterns. Hehuang Xie and colleagues defined “methylation entropy” and exploited it to assess the variability of patterns that might be observed.