Friday, June 23, 2023
A new approach could provide insight into cancer progression and response to treatment, leading to more precise therapies.
Using 3D models of ovarian cancer tumors, scientists have found differences in gene activity depending on where a cell is located within a tumor, demonstrating how location and environment of a cell in a cancerous tumor can strongly influence active genes and the cell’s role in cancer biology. Specifically, the team co-led by researchers from the National Center for Advancing Translational Sciences (NCATS), part of the National Institutes of Health, showed that gene activity in cells on or near the surface the surface of a tumor differed from that of cells closer to the tumor center.
The approach combines the use of technology to reveal the genetic activity of individual cells within a tumor with fluorescent dyes that propagate within tumors. The work could allow researchers to study how the same diseases can vary in people and progress differently. This research could help clinicians identify treatment strategies targeted at specific areas of tumors, which could lead to better therapies for cancers and other diseases. The team reported its results on June 21 in Cellular systems.
“It is commonly believed that a cell’s location and the surrounding environment influence cell identity,” said Craig Thomas, Ph.D., translational researcher at NCATS. “Two cells can be genetically identical but have different cell identities, which means that different genes are activated due to their location and environment. Our goal was to establish a simple method to study this concept in several contexts.
The new system, called segmentation by exogenous perfusion, or SEEP, takes advantage of a dye that diffuses into cells of a tumor at a definable rate. Measuring the amount of dye entering individual tumor cells provides information about the cell’s location and, more specifically, its access to the external environment. Using computational methods, the researchers linked this information to the activity of cell genes, allowing scientists to link cell identities to their location.
“Understanding the relationship of cells to each other and the effects of their positions in space has been a fundamental question in cancer, neurological disorders, and other fields,” said co-author Tuomas Knowles, Ph.D. , at the University of Cambridge.
The researchers used three types of 3D laboratory models – spheroids, organoids and mouse models – created from human ovarian cancer cells. Spheroids are 3D clumps of cells grown in a laboratory dish that can mimic certain organ and tissue traits. Organoids, also cultured in a dish, are more complex 3D models that more closely mimic the function and structure of organs and tissues. In mouse models, researchers implanted human ovarian cancer cells to form tumors.
“It’s critical to understand that not all cells in a tumor will be exposed to a drug in the same way,” Knowles said. “A cancer drug can kill the cells on the surface of a tumor, but the cells in the middle are different and affected differently. This probably contributes to the failure of some therapies.
The SEEP method revealed that tumor cells close to the surface of the tumor were more likely to undergo cell division than cells closer to the center of the tumor. Cells on the surface of tumors also turn on genes to protect them from immune system responses. Unsurprisingly, these gene responses are linked to how the tumor hides from the body’s immune defences.
The researchers were surprised by the differences in gene activity between cells located on or near the surface and those located further inside the ovarian cancer tumor models. The findings could help scientists better understand how tumors are structured. This information could lead to improved treatments. One possible method of treating cancer could be to target cells that are likely to be affected in different areas of tumors.
“Certain types of tumor cells are sensitive to certain therapies,” noted first author and Harvard University medical student David Morse, Ph.D. the tumor could help us decide how to use the drugs in combination. This could help us know how long to give a drug and when to switch to other therapies.
The NIH research was supported by the NCATS Division of Preclinical Innovation and the Center for Cancer Research at the National Cancer Institute. The work at Harvard University was funded by the National Science Foundation (DMR-1708729) and the Harvard Materials Research Science and Engineering Center (DMR-2011754). Research at the University of Cambridge has been supported by the Biotechnology and Biological Sciences Research Council, the Newman Foundation, the Wellcome Trust and the European Research Council under the European Union’s Seventh Framework Program (FP7 / 2007-2013) thanks to the grant of the European Research Council PhysProt (convention n° 337969). There has also been support from the NIH Oxford – Cambridge Scholars Program and the Certara Biomedical Research Scholarship.
About the National Center for the Advancement of Translational Sciences (NCATS): NCATS conducts and supports research into the science and workings of translation – the process by which interventions to improve health are developed and implemented. – to allow more treatments to reach more patients faster. For more information on how NCATS helps shorten the journey from scientific observation to clinical intervention, visit https://ncats.nih.gov.
About the National Institutes of Health (NIH):The NIH, the country’s medical research agency, comprises 27 institutes and centers and is part of the US Department of Health and Human Services. The NIH is the primary federal agency that conducts and supports basic, clinical, and translational medical research, and studies the causes, treatments, and cures for common and rare diseases. For more information about the NIH and its programs, visit www.nih.gov.
NIH…Transforming Discovery into Health®
DB Morse, et al. Positional influence on cellular transcriptional identity revealed by spatially segmented single-cell transcriptomics. Cellular systems DOI: https://doi.org/10.1016/j.cels.2023.05.003