Healthcare (Commonwealth Union) – The spread of tumors from their original cancer site to other parts of the body, known as metastasis, has long baffled researchers. Only recently have scientists begun to uncover the triggers and mechanisms driving this complex process. A new study in India conducted by the Indian Institute of Science (IISc) sheds light on how intrinsic differences in cancer cells and their interactions with their surroundings influence their movement. Published in the Biophysical Journal, the research highlights that cancer cells adjust their migration patterns based on the physical and biochemical properties of their surrounding environment, known as the microenvironment.
The team examined two types of ovarian cancer cells: OVCAR-3, characterized by a structured, polygonal shape, and SK-OV-3, which exhibits a naturally elongated spindle shape. Both cell types are capable of metastasizing and invading tissues. To understand their behavior, the researchers placed the cells on surfaces with varying stiffness—soft surfaces resembling healthy tissue and stiff surfaces mimicking the fibrous, scarred tissue often found around tumors.
Researcher of the study indicated that on soft surfaces, both cell types saw movement slowly and in random directions, much like they would in healthy tissue. However, on stiff surfaces resembling the rigid environment around tumors, the cells displayed greater deformability and distinct responses.
Madumitha Suresh, a former MTech student from the Department of Bioengineering and the study’s lead author indicated that from prior studies, it was anticipated that stiffness would play a significant role in enhancing cancer cell migration. What surprised Suresh was that the epithelial ovarian cancer cells (OVCAR-3) were more migratory than the mesenchymal cells (SK-OV-3) on stiffer surfaces.
The researchers identified a distinctive movement pattern in OVCAR-3 cells, which they referred to as “slip.” Typically, a cell’s movement direction aligns with its shape, with the “front” of the cell leading the way. However, when OVCAR-3 cells moved on stiff surfaces, this coordination broke down. Their motion became disconnected from their shape, resembling a sliding or slipping action rather than straightforward movement. These surprising findings inspired the researchers to investigate further using quantitative methods.
Traditional techniques for studying cancer cell movement have significant drawbacks. Mathematical approaches often fail to capture dynamic changes, while advanced computational methods, like machine learning, are complex and difficult to implement. To overcome these challenges, the researchers developed a user-friendly software toolkit that integrates Shannon entropy—a measure of randomness—with established metrics for cell movement and shape. This toolkit enables researchers to track and quantify changes in cell behavior over time, turning live cell data into numerical insights rather than relying solely on verbal interpretations. By carefully analyzing the correlations between the toolkit’s metrics, the team uncovered how the epithelioid cancer line demonstrates a looser connection between its shape and movement, allowing it to migrate in diverse and unexpected ways.
“We aim to extend our study to decipher the collective dynamics of such cancer cells, especially in more complex 3D environments,” explained Ramray Bhat, who is an Associate Professor at the Department of Developmental Biology and Genetics and corresponding author of the study. “This will shed fresh light on the pathology of ovarian cancer, a disease that is characterised by rapid metastasis and high morbidity.”
Cancer research has always been at the forefront of scientific innovation, but recent technological advancements are propelling the field into a new era. From artificial intelligence to advanced imaging and biotechnology, these innovations are not only accelerating the pace of discovery but also reshaping how we understand and treat cancer.
Artificial intelligence (AI) and machine learning are becoming indispensable tools in cancer research. By analyzing massive datasets from clinical trials, genetic sequencing, and patient records, AI can identify patterns that human researchers might miss.
