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Minnesota: New Integrated Data Analysis Method to Improve Cancer Cell Research and Treatments

The University of Minnesota School of Public Health is developing a new method for identifying important molecular changes to cancer cells that could lead to improved treatments and help more patients defeat the disease. The study, called “Bidimensional integration for pan-omics pan-cancer analysis” is led by assistant professor, Dr. Eric Lock and funded by a grant from the National Cancer Institute.

Pan-cancer analysis refers to the evaluation of scientific data across different types of cancers, for example, breast, lung, and skin cancer. Pan-omics is the analysis of data, called “omes,” that describe different molecular aspects of a cell. An example of a cell ome is its genome, which details its DNA chemical composition. Examining the genome and other data can reveal mutations that trigger healthy cells to become cancerous.

“While most studies focus on a single type of cancer or a single cell ome, we’re developing new statistical methods that allow for the comprehensive integration and analysis of multiple cancer types and cancer cell omic data,” said Dr. Lock. “Using all of the available data together will give us more power to detect molecular changes to cells that are clinically meaningful, which can be used to guide the treatment of cancers.”

Dr. Lock’s approach will search for key mutations with two particular aims:

The approach will be developed and tested on data from The Cancer Genome Atlas Project, which includes several kinds of omics data for more than 10,000 cancer cases representing 33 different types of cancer.

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