Most cancer cells grown on a plate have little in common with people’s cancer cells.


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In an attempt to find or refine laboratory cancer research models that best compare to what happens in living people, scientists at Johns Hopkins Medicine report that they have developed a new computer-based technique that proves that cancer cells humans grown in culture dishes are the least genetically similar. to their human sources.

They say the finding should help focus more resources research models such as genetically modified mice and 3D human tissue balls known as “tumors” to better assess the biology and treatments of human cancer, and the genetic errors responsible for cancer growth and progress.

“This may not be a surprise to scientists they are genetically inferior to other models, but we were surprised that genetically modified mice and tumors performed so well in comparison, ”says Patrick Cahan, Ph.D., associate professor of biomedical engineering at Johns Hopkins University and Johns Hopkins University School of Medicine and principal investigator of the new study.

The new technique, called CancerCellNet, uses computer models to compare the RNA sequences of a research with data from a to compare the extent to which the two sets coincide.

The researchers found that, on average, genetically modified mice and tumors have RNA sequences more closely aligned with baseline genome atlas data in 4 out of 5 tested, including breast, lung, and ovarian cancers.

Researchers say their work adds to cancer testing grown in the laboratory have less parity with their human source due to the complex differences between the natural environment of a human cell and a growth environment of the laboratory. “Once you take the tumors out of their natural environment, the cell lines start to change,” Cahan says.

Scientists around the world rely on a number of research models to improve their understanding of cancer and other biological diseases and to develop treatments for conditions. Among the most widely used cancer research models are cell lines created by extracting human tumor cells and culturing them with various nutrients in laboratory flasks.

Researchers also use mice that have been genetically modified to develop cancer. In other cases, they implant human tumors in mice, a process called xenografting, or they use tumors.

To assess the degree of alignment of these research models with what can happen to humans, scientists usually transplant cells cultured in the laboratory or tumor cells or xenografts into mice and check whether the cells are they behave as they should, that is, they grow and spread and preserve the genetic characteristics of cancer. However, Johns Hopkins researchers say this process is expensive, time consuming, and a scientific challenge.

The aim of the new work was to develop a computational approach to evaluate research models in a less cumbersome and accurate way. A report on the work was published on April 29 a Genome medicine, and researchers have applied for a provisional patent on what they called CancerCellNet.

The new technique is based on on cellular RNA, a molecular chain of DNA-like chemicals, and an intermediate set of instructional cells used to translate DNA into protein manufacturing.

“RNA is a surrogate substitute for cell type and cell identity, which are key to determining whether cells developed in the lab look like their human counterparts,” says Cahan. “RNA expression data are highly standardized and available to researchers and are less subject to technical variations that may confuse the results of a study.”

First, Cahan and his team had to choose a standard data set that would act as a basis for comparing research models. Atlas data from the cancer genome served as so-called “training” data, which includes information on the RNA expression of hundreds of patient tumor samples and their corresponding stage, grade, and other information. tumor.

They also tested their CancerCellNet tool by applying it to data where the type of tumor was already known, such as the International Human Genome Sequencing Consortium.

Research team members combed through data from the Cancer Genome Atlas to determine 22 types of tumors to study. They used genome atlas data as a reference to compare RNA expression data from 657 cancer cell lines grown in laboratories around the world, some of which were established decades ago, 415 xenografts, 26 genetically modified mouse models and 131 tumoroids.

In one example from the study, prostate cancer from a line called PC3 it begins to look more like genetically bladder cancer, he points out. According to him, it is also possible that the cell line was originally incorrectly labeled or that it may actually have been derived from bladder cancer. But the conclusion is that, from a genetic point of view, the prostate cancer cell line was not a substitute for what happens in a typical human with prostate cancer.

The researchers found that, using a 0-1 scoring method, cell lines had, on average, a lower scoring alignment than atlas data than tumors and xenografts.

Cahan says he and his team will add additional RNA sequencing data to improve the reliability of CancerCellNet.

Use gene expression data to compare laboratory cancer models with real tumors

More information:
Da Peng et al, Evaluation of the transcriptional fidelity of cancer models, Genome medicine (2021). DOI: 10.1186 / s13073-021-00888-w

Citation: Most cancer cells grown on a plate have little in common with people’s cancer cells (2021, June 17) recovered on June 17, 2021 at 2021-06-cancer-cells-grown-dish- common.html

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