Researchers at the Sol Goldman Pancreatic Cancer Research Center at Johns Hopkins Kimmel Cancer Center have pioneered a 3D genomic profiling technique to identify small precancerous lesions, known as pancreatic intraepithelial neoplasias (PanINs), in the pancreas. These lesions can lead to pancreatic ductal adenocarcinoma (PDAC), one of the most aggressive forms of pancreatic cancer. Their study, published on May 1 in Nature, provides the most detailed 3D map of these precancerous lesions to date, offering a foundation for early detection of PDAC and other pancreatic cancers.

Figure 1. Pancreatic Cancer. (Credit: Ashley Kiemen)

Laura Wood, M.D., Ph.D., associate professor of pathology and oncology at the Kimmel Cancer Center and the Johns Hopkins University School of Medicine, and a senior author of the study, expressed surprise at the high number of PanINs found in normal pancreatic regions. This research highlights the gaps in our understanding of normal aging and the development of cancer in the pancreas. Figure 1 shows pancreatic cancer.

The study was co-led by Alicia Braxton, D.V.M., Ph.D., a postdoctoral fellow, and Ashley Kiemen, Ph.D., an assistant professor of pathology, both from Johns Hopkins University School of Medicine.

PanINs are difficult to detect due to their small size and are not identifiable through typical radiology exams. Consequently, many patients are diagnosed with pancreatic cancer only when it has already reached an advanced and metastatic stage.

Traditional 2D histological staining methods provide a limited view of PanINs, restricting researchers' understanding of their origins and progression to cancer. To overcome this, the researchers developed a 3D approach. They thinly sliced and stained tissue from 38 normal pancreatic samples onto hundreds of sequential 2D slides, then used a machine-learning pipeline called CODA to analyze and reconstruct these images into 3D digital models.

These 3D reconstructions revealed complex networks of interconnected PanINs, averaging 13 PanINs per cubic centimeter, with a range from 1 to 31 PanINs per cubic centimeter. Patients with PDAC in other pancreatic regions had a higher PanIN burden, though this was not statistically significant [1].

Further analysis of eight samples through 3D-guided microdissection and DNA sequencing revealed that these networks comprised genetically distinct PanINs driven by different mutations, including mutations in the cancer-causing gene KRAS, commonly found in pancreatic cancers. This discovery that multiple precancerous lesions can arise from independent mutations is unprecedented in other organs, according to Wood. This finding opens new avenues for targeting these lesions, potentially through KRAS-focused therapies.

Although CODA is not yet ready for clinical diagnostics, Kiemen noted its versatility for application to any tissue, disease, or model organism. She emphasized the importance of continuing research to understand which PanINs are clinically relevant to disease.

Wood concluded that prevention and early detection through detailed 3D molecular maps of cancer precursors could significantly impact cancer treatment. This study marks the beginning of deeper explorations into the early stages of cancer development.

Source: Johns Hopkins Medicine

References:

1. https://www.eurekalert.org/news-releases/1048677

Cite this article:

Hana M (2024), Advancements in 3D Genomic Profiling for Early Pancreatic Cancer Detection, AnaTechMaz, pp. 263