Brown University researchers, including Susan Leggett, a doctoral Pathobiology student, have developed a new image analysis technique to distinguish two key cancer cell types associated with tumor progression. The approach could help in pre-clinical screening of cancer drugs and shed light on a cellular metamorphosis that is associated with more malignant and drug-resistant cancers.
The epithelial-mesenchymal transition, or EMT, is a process by which more docile epithelial cells transform into more aggressive mesenchymal cells. Tumors with higher numbers of mesenchymal cells are often more malignant and more resistant to drug therapies. The new technique combines microscopic imaging with a machine learning algorithm to better identify and distinguish between the two cell types in laboratory samples.
“We know that there are these different cell types interacting within tumors and that therapeutics can target these cells differently,” said Leggett, the lead author of a paper describing the technique. “We’ve developed a model that can pick out these cell types automatically and in an unbiased way. We think this could help us better understand how these different cell types respond to drug treatment.”
The technique is described in an article published in Integrative Biology. Generally speaking, the two cell types can be distinguishable by their shapes. Epithelial cells are more compact in appearance, while mesenchymal cells appear more elongated and spindly, both in their overall appearance and in the appearance of their nuclei.