Christopher Plaisier, an assistant professor of biomedical engineering in Arizona State University’s Ira A. Fulton Schools of Engineering, and Samantha O’Connor, a biomedical engineering doctoral student in the Plaisier Lab, are leading research into a new stage of the stem cell life cycle that may hold the key to unlocking new methods of brain cancer treatment. Their findings were recently issued in the journal Molecular Systems Biology.
Developing New Treatments Of Tumor Through Understanding Stem Cell Resting Phase
Plaisier adds that the cell cycle is such a well-studied phenomenon, and yet here they are looking at it for the umpteenth time, and a new phase leaps out at them. Biology is continually revealing fresh ideas; all they have to do is look.
A partnership with Patrick Paddison, who is an associate professor at the Fred Hutchinson Cancer Research Center in Seattle, and Dr. Patel, who is an assistant professor of neurological surgery at the University of Washington who is also involved with the Fred Hutchinson Cancer Research Center, sparked this discovery.
Paddison’s team enlisted Plaisier’s assistance in analyzing their brain stem cell data, which had been described using a technique known as single-cell RNA sequencing.
Plaisier describes the findings as very remarkable. It formed a lovely circular pattern, which they identified as all of the distinct phases of the cell cycle.
O’Connor created a new cell cycle classifier tool called ccAF (cell cycle ASU/Fred Hutchinson to represent the links between the two institutions) that takes a closer, high-resolution look at what’s going on within stem cell growth cycles and identifies genes that can be used to track the progress through the cell cycle.
Their classifier goes deeper into the cell cycle because they may be collecting parts that have major consequences for illness, according to O’Connor.
Plaisier and O’Connor discovered that when they utilized the ccAF method to evaluate cell data for glioma tumors, the tumor cells were frequently in the Neural G0 or G1 development state. As cancers progress, fewer and fewer cells remain in the resting Neural G0 stage. This implies that the tumor is expanding as more cells proliferate.
They used this information to predict the prognosis of individuals with glioblastoma, a particularly aggressive kind of brain tumor. Those that had higher levels of Neural G0 in their tumor cells had less aggressive tumors.
They also discovered that the quiescent Neural G0 stage is unaffected by a tumor’s proliferation rate, or how quickly its cells divide and proliferate again.
According to Plaisier, one of their findings was that quiescence itself may be a separate biological activity. It’s also a possible location for them to seek new medication therapies. The cancers would become less aggressive if they could get more cells to enter a dormant condition.
Cancer drugs now on the market are designed to destroy cancer cells. When cancer cells are destroyed, however, they leak cell debris into the tumor’s surrounding region, which can lead the surviving cells to grow more resistant to the medicines.
Plaisier believes that instead of destroying the cells, putting them to sleep might result in a far better scenario.
They were also able to discover additional states at the beginning and conclusion of the cell cycle that exists between the generally recognized stages using their ccAF program. These are some of the subjects under consideration for their future round of research.
They are beginning to consider methods to go into them and understand more about the biology of cell cycle entry and departure since they are potentially critical moments when cells will either enter or exit the cell cycle, according to Plaisier.
Understanding what causes a cell to enter the division cycle or remain in a G0 resting-state might aid in understanding tumor development mechanisms.
According to Plaisier, the main characteristic of any malignancy is cell proliferation. If they can get in there and discover out what the processes are, it may be a good location to slow them down.
Plaisier and O’Connor are making the ccAF classifier tool open source and available in a number of formats to help anybody studying single-cell RNA sequencing data get started with cell cycle research.