FOR IMMEDIATE RELEASE
Newswise — Researchers with the Johns Hopkins Kimmel Cancer Center and the Johns Hopkins University School of Medicine received a $3 million grant to use computational modeling and software to understand biological data, in combination with unique in vitro and animal studies, to better treat liver cancer.
The project, “Integrating bioinformatics into multiscale models for hepatocellular carcinoma,” is led by Elana Fertig, Ph.D., assistant professor of oncology and assistant director of The Research Program in Quantitative Sciences, Aleksander Popel, Ph.D., director of the Systems Biology Laboratory and professor of biomedical engineering, oncology, and medicine, Andrew Ewald, Ph.D., professor of cell biology, oncology, and biomedical engineering, and Phuoc Tran, M.D., Ph.D., associate professor of radiation oncology and molecular radiation sciences, oncology and urology.
“This is an unprecedented combination of four disciplines in an integrative and interactive way,” Popel said. “In this project, we hope to better identify new targets for treatment of liver cancer and gain an improved understanding on how current therapies are working.”
The five-year grant was awarded by the National Cancer Institute, part of the National Institutes of Health, in April 2018.
Hepatocellular carcinoma, or liver cancer, occurs when a tumor grows on the liver. It is responsible for over 12,000 deaths per year in the United States, making it one of the most common cancers in adults.
The research team is looking into new computational techniques to build models that will predict therapeutic responses to liver cancer, addressing a dire and unmet need to improve treatment. The data for the models will use information about molecules, cells, tumors, and organs learned from state-of-the-art 3D in vitro models and in vivo animal models of hepatocellular carcinoma.
The proposal will result in new algorithms for predictive computational modeling of therapeutic response in hepatocellular carcinoma. The resulting computational algorithms will address the challenge of molecular alterations that occur on a different time scale than cellular changes in different areas of study.
Work is already underway on the project to help clinicians find new ways to further develop treatment options.