Detailed, Automated 3D Imaging of Pancreatic Cancers and Precancers

ASPIRE Award (2019-Present)

Richard Levenson, MD (Co-Principal) and Farzad Fereidouni, PhD (Co-Principal), University of California, Davis; Ralph Hruban, MD; Denis Wirtz, PhD; Laura Wood, MD, PhD; and Pei-Hsun Wu, PhD, Johns Hopkins University

The outlook for pancreatic cancer is grim for most patients diagnosed with this deadly disease. The reasons for this devastating prognosis are poorly understood, but recent research suggests that the cancer is aggressive because of the unique ability of pancreatic tumor cells to invade neighboring veins. Scientists’ understanding of this process is hampered by a lack of simple, inexpensive 3D imaging techniques that allow doctors to pinpoint why, how, and where the cancer cells invade veins. Researchers in the Fereidouni and Levenson labs are developing a 3D imaging technique called 3D Microscopy with Ultraviolet Surface Excitation (3D-MUSE) to generate detailed images of pancreatic tumor specimens provided by the biobank at Johns Hopkins University. The researchers at UC Davis are building two, fully automated 3D-MUSE instruments that can stain, slice, and image tissue samples. They are also developing new procedures for staining samples as well as accompanying machine-learning software to assemble slices into 3D images of the whole tissue and to analyze these images. The researchers at Johns Hopkins will work in parallel to determine whether 3D-MUSE can be used to pinpoint the location where tumor cells enter the vascular system. Once they have located this critical point, they will work to fully characterize the cell types found there based on their morphology and molecular signatures. The team expects that these studies will provide important insight into how invasion of nearby veins affects the ability of pancreatic tumors to metastasize. Once validated, they also intend to use 3D-MUSE to analyze pancreatic cancer precursor lesions. In the future, 3D-MUSE could become a standard tool for providing in-depth, 3D imaging of a variety of tissue samples.