Lab-grown cancer cells have less parity with their human source
A new computer-based technique developed by scientists at Johns Hopkins University School of Medicine has revealed that human cancer cells grown in culture dishes are the least genetically similar to their human sources. The new technique – a tool called CancerCellNet – uses computer models to compare the RNA sequences of a research model with data from a cancer genome atlas to compare how closely the two sets match up.
Cellular RNA is a molecular string of chemicals similar to DNA. It is an intermediate set of instructions cells use to translate DNA into the manufacture of proteins. “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,” said Dr. Patrick Cahan, associate professor of biomedical engineering at the prestigious university.
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Scientists worldwide rely on a range of research models to improve their understanding of cancer and other disease biology and develop treatments. Among the most widely-used cancer research models are cell lines created by extracting cells from human tumours and growing them with various nutrients in laboratory flasks. Researchers also use genetically engineered mice that develop cancer or implant human tumours into mice, a process called xenografting, or use 3D balls of human tissue known as tumouroids.
Scientists may also transplant lab-cultured cells or cells from tumouroids or xenografts into mice and see if the cells behave as they should—that is, grow and spread and retain the genetic hallmarks of cancer. However, the Johns Hopkins scientists say the process is expensive, time-consuming and scientifically challenging.
The scientists instead found that genetically engineered mice and tumouroids have RNA sequences most closely aligned with a set of genetic baseline data in 4 out of every 5 tumour types tested in their study, including breast, lung and ovarian cancers; and using a 0-1 scoring method, cell lines had, on average, lower scoring alignment to the baseline data compared to tumouroids and xenografts.
In one example from the study, prostate cancer cells from a line called PC3 started to look genetically more like bladder cancer, but ultimately was not a representative surrogate for what happens in a typical human with prostate cancer, Dr. Cahan said.
The goal of the new work was to develop a computational approach to evaluating research models in an accurate and a less cumbersome way. Dr. Cahan and his team will still be adding additional RNA sequencing data to improve the reliability of CancerCellNet.
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