The research coming out of Purdue University and Lawrence Berkeley National Laboratory is touted as a new revolutionary imaging way to find early neoplastic changes. The method works via analysis of intracellular nuclear protein distribution. The research was conducted by Sophie Lelièvre, Purdue assistant professor of basic medical sciences and David Knowles from Lawrence Berkeley National Laboratory:
“When you look at cells that don’t yet have a specific function – aren’t differentiated, compared to fully differentiated cells, which are now capable of functioning as breast cells – the organization of proteins in the nucleus varies tremendously,” Lelièvre said. “Then looking at how the proteins in malignant cells are distributed, it’s a totally different pattern compared to normal differentiated cells.”
The research team’s study on the imaging technique and its use in 3-D mapping and analysis of nuclear protein distribution is published this week online in Proceedings of the National Academy of Sciences. Ultimately, the scientists want to use the technique to determine not only if a lesion is malignant but also the exact kind of cancer, how likely it is to spread and the most appropriate treatment for a particular patient.
“The major problem exists in the pre-malignant stages of abnormal cells in determining whether cancer will develop, what type and how invasive it will be,” Lelièvre said. “The decision then is whether to treat or not to treat and how to proceed in these preliminary stages because only a certain percentage of these patients will ultimately develop cancer.
“We want to use this technique to identify subtypes of cells within lesions that potentially could become more aggressive forms of cancer.”
Lelièvre, Knowles and their team used an antibody attached to a fluorescent molecule that targeted and linked with a specific nuclear protein from mammary tissue. When malfunctioning, this protein, named nuclear mitotic apparatus protein (NuMA), has been linked to leukemia and breast cancer.
The imaging technique the researchers developed to identify NuMA location shifts is called an automated local bright feature image analysis. It recorded the average amount of luminescence throughout the nucleus and then located the brightest spots, which were the protein. The system then automatically measured the differences in the protein’s distribution in each cell type and mapped it. This enabled the researchers to verify the changes exhibited by non-differentiated cells that were still multiplying, normal mammary cells and multiplying malignant cells.
The ability to see the protein patterns in the nucleus gives scientists one more tool in advancing the identification of types of cancer and appropriate treatment, Lelièvre said. The imaging tool should work for mapping and analyzing locations of any nuclear protein.