Project 451861

Cellular ecosystem modelling of liver cancer

451861

Cellular ecosystem modelling of liver cancer

$1,071,000
Project Information
Study Type: Unclear
Research Theme: Biomedical
Institution & Funding
Principal Investigator(s): Bader, Gary D
Co-Investigator(s): MacParland, Sonya; Mcgilvray, Ian D; Michalak, Thomas I
Institution: University of Toronto
CIHR Institute: Genetics
Program: Project Grant
Peer Review Committee: Genomics: Systems and computational biology
Competition Year: 2021
Term: 5 yrs 0 mth
Abstract Summary

The recent breakthrough technology of single-cell genomics has vastly improved our capacity to understand how the trillions of cells in our body develop and work together to make us human. This advance combines the power of the microscope, to discover that all biological organisms are composed of cells, with the power of DNA sequencing to map the human genome. Thus, with single-cell genomics, we can not only map cells, but measure vast quantities of molecular information about them by the millions. A large number of fundamental scientific challenges, such as how a single germ cell gives rise to every cell in the human body and how diseases develop within our cells, are now much closer to being solved. In this proposal, we will develop a new way to model disease, as an ecosystem of cells. Using a combination of state-of-the-art computational analysis methods and experimental single-cell-resolution genomic tissue mapping technology, we will study the development of hepatitis-induced liver cancer to learn more about which cells are affected and how the disease develops from start to finish. Liver cancer affects thousands of Canadians and has few treatment options. The most effective treatment of advanced disease is a liver transplant which is expensive and requires a healthy donor liver. This project will help us to discover new liver cancer disease mechanisms that can be targeted with novel therapies, applied at earlier stages, to ideally stem the course of tumour growth and the development of advanced disease.

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Keywords
Computational Biology Liver Cancer Single Cell Genomics Systems Biology