Researchers at the Johns Hopkins Kimmel Cancer Center’s Sol Goldman Pancreatic Cancer Research Center have developed a 3D genomic profiling technique to identify small precancerous lesions in the pancreas — called pancreatic intraepithelial neoplasias (PanINs) — that lead to one of the most aggressive, deadly pancreatic cancers.
The study was published May 1 in Nature.
After thinly slicing and staining tissue from 38 normal pancreatic samples onto hundreds of sequential 2D slides, the researchers developed CODA, a machine-learning pipeline, to analyze and reconstruct the slide images into digital 3D images.
The 3D reconstructions revealed complex networks of interconnected PanINs with an average overall burden of 13 PanINs per cubic centimeter, and a range of from 1 to 31 PanINs per cubic centimeter. Patients with PDAC in other regions of their pancreas seemed to have a higher PanIN burden than those with nonductal disease, although it was not statistically significant.
The researchers further investigated eight of the samples via 3D-guided microdissection and DNA sequencing of specific PanINs. Genomic analysis revealed that the networks were made up of genetically distinct PanINs driven by different gene mutations, such as mutations in the cancer-causing gene Kirsten rat sarcoma virus (KRAS), which is found in most pancreatic cancers.
