Scientists Discover Influence of Location and Environment on Gene Expression in Ovarian Cancer

A cell’s position and surroundings within a cancerous tumor can significantly affect which genes are active and the cell’s role in the disease’s biology. This was demonstrated by researchers utilizing 3-D models of ovarian cancer tumors to find changes in gene expression based on where a cell is in a tumor.

In more detail, the group, which was co-led by scientists from the National Centre for Advancing Translation Sciences (NCATS), a unit of the National Institutes of Health, demonstrated that the gene activity of cells on or near a tumor’s surface differed from that of cells located closer to the tumor center. 

The method combines fluorescent colors that penetrate tumors with a device that can show the molecular activity of individual cells within a tumor. Research on how similar diseases might vary across individuals and develop in various ways may be made possible by this work. Better treatments for cancer and other disorders could result from this research, which could aid medics in identifying treatment plans that target certain tumor regions. 

A translational scientist at NCATS (National Centre for Advancing Translational Sciences), Craig Thomas, Ph.D., stated that it is “generally accepted that a cell’s site and surrounding environment influence the cell’s identity.” The location and environment of two cells might cause them to have distinct cellular identities despite sharing the same genetic makeup. We aimed to develop an easy procedure for investigating this idea in many contexts. 

The brand-new technique, Segmentation by Exogenous Perfusion, or SEEP uses a dye that diffuses into tumor cells at a predetermined rate. Individual tumor cells can be identified by the amount of dye that enters them; this reveals where the cell is located and, more particularly, how accessible it is to the outside world. The scientists could link the cells’ identities with their locations by using computational techniques to link this data to gene activity in the cells. 

“Understanding the relationship cells have to one another, and the impact of how they move in space remains an essential issue in cancer, neurological disorders, and other areas,” said co-author and University of Cambridge Ph.D. Tuomas Knowles. 

The researchers used spheroids, organoids, and animal models, all made from human ovarian cancer cells. Spheroids are 3D cell clusters that can imitate some characteristics of organs and tissues. They are generated in a lab dish. Organoids are more intricate 3-D models that more closely resemble the structure and function of organs and tissues. Organoids are also developed in a dish.  

Researchers injected human ovarian cancer cells to create tumors in the mice models. Knowing that not all tumor cells will be exposed to treatment similarly is crucial, according to Knowles. “A cancer treatment may kill the tumor’s outside cells, but the inside cells are different and treated differently. It is probably a factor in some medicines failing. 

According to the SEEP approach, tumor cells near the tumor exterior were more inclined to go through cell division than tumor cells farther from the tumor center. Additionally, tumor cells activate genes to defend themselves from immunological responses. It should be no surprise that these genetic responses are connected to the tumor’s ability to evade the body’s immune defenses. 

The variations in gene expression among cells close to the surface and those deeper within the ovarian cancer tumor models astonished the researchers. The results might improve our knowledge of how tumors are built. This knowledge might result in better medical interventions. Targeting the cells most likely to be impacted in various tumor regions could be one potential cancer treatment strategy. 

According to the first author and medical student at Harvard University, David Morse, Ph.D., “certain tumor cell types are susceptible to specific therapies.” “Knowing where cells are located and how accessible they are within the tumor may help us choose the best treatment combinations. It might indicate when to switch to other treatments and how long to provide a medicine.