Cooper’s contribution focused on the analysis of microscopic images of cancer tissues. Pathologists examine these tissues using a microscope to classify and determine how advanced a patient’s disease is, but the molecular basis of many of the patterns that they see in tissues are not well understood.

The team found that tumors with more variability were also more complex on a molecular level, having more abnormal chromosomes and genetic diversity within their cells. To perform this analysis, Cooper’s team applied image analysis techniques to analyze the size and shape of more than 500 million cells in sarcoma tissues from the TCGA and compared these measurements to data obtained from DNA sequencing.

“Pathologists have used the variability of the size and shape of cells as a prognostic indicator in many cancers, but the molecular associations of this variability are not well understood,” said Cooper. “The blend of computational and clinical expertise on the TCGA teams is really unique. This expertise and the datasets produced by TCGA allow us to investigate the molecular foundations of visual patterns that pathologists routinely use to classify disease.”

Cooper’s lab is currently extending this approach to understand patterns that pathologists see in other diseases.

Media Contacts:

Walter Rich

Communications Manager

Wallace H. Coulter Department of Biomedical Engineering

Georgia Institute of Technology