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Study Finds Dish-Grown Cancer Cells Don't Reflect the Cancer Cells in People

A new study is aiming to revolutionize how we approach cancer research.

In the fight against cancer, scientific research is key. But it turns out not all research models are created equal.

One of the most widely used cancer research models is cell lines created by extracting cells from human tumors and growing them in laboratory flasks. Now, a recent study by Johns Hopkins Medicine scientists is revealing that this type of approach leads to cancer cells that are significantly different from those found in humans.

“It may not be a surprise to scientists that cancer cell lines are genetically inferior to other models, but we were surprised that genetically engineered mice and tumoroids performed so very well by comparison,” Patrick Cahan, associate professor of biomedical engineering at The Johns Hopkins University and the Johns Hopkins University School of Medicine and lead investigator of the new study, said in a statement.

Tumoroids are 3D balls of human tissue and genetically engineered mice refer to mice grown to have cancer.

To conduct their study, the researchers developed a new computer-based technique that compares the RNA sequences of a research model with data from a cancer genome atlas titled, well, The Cancer Genome Atlas. They dubbed their new technique CancerCellNet.

“RNA is a pretty good surrogate for cell type and cell identity, which are key to determining whether lab-developed cells resemble their human counterparts,” explained Cahan. “RNA expression data is very standardized and available to researchers, and less subject to the technical variations that can confound a study’s results.”

CancerCellNet compared RNA expression data from 657 cancer cell lines grown in labs worldwide, 415 xenografts (a process that sees human tumors implanted into mice), 26 genetically engineered mouse models, and 131 tumoroids.

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"By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types," conclude the researchers in their study.

However, it should be noted that there are significantly fewer samples of tumoroids and genetically engineered mouse models which could potentially affect the data. The researchers don't seem to take this difference into consideration but do note that they will keep adding additional RNA sequencing data to improve the reliability of CancerCellNet.

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