Cancer Cells Grown in the Lab Are Found to Be Very Distinct from Those Found in Humans
The study of cancer cells grown in culture dishes is critical for developing more effective treatments and working toward a potential cure for this insidious disease – but new research exposes some critical genetic variations between these cells and cancer cells that grow in the human body.
While this does not preclude relevant and enlightening laboratory research utilizing lab-grown cells, it is critical for scientists to understand these distinctions as they investigate approaches to prevent tumors from spreading and inflicting damage.
The researchers developed a machine learning model called CancerCellNet (CCN) to compare cancer cells found in the body to cancer cells from four additional sources: 26 mice models engineered to develop cancer; 415 mice with human cancer cells transplanted (xenografts); 131 balls of 3D tissue grown in the lab to mimic tumors (tumoroids); and 657 traditional cancer cell lines (cancer cells grown in culture dishes).
By comparing the RNA sequences of these cells — the biological instructions that dictate how proteins evolve – to a cancer genome database, the researchers were able to assess their genetic similarity to in vivo tumors.
“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,” says Johns Hopkins University molecular biologist and geneticist Patrick Cahan.
On average, the genetically altered mice and tumoroids had RNA sequences that were most closely related to those found in true human cancer in almost 80% of the tumor types evaluated, including breast, lung, and ovarian cancers.
Cancer cell lines fared less well, with greater disparities between them and previously described human malignancies. In one instance highlighted in the study, a prostate cancer cell line known as PC3 resembled bladder cancer more closely. Cell lines appear to undergo transformation once they are removed from their natural environment.
“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,” Cahan explains.
“RNA expression data is very standardized and available to researchers, and less subject to technical variation that can confound a study’s results.”
CancerCellNet’s advantages include its versatility and speed: it is unquestionably faster and less expensive than transferring tumours into mice to see their development, which is one way now used by scientists to compare different models.
Bear in mind that the study has limitations. While RNA is an excellent method for comparing cells, it does not tell the entire picture, and the researchers wish to augment their CCN training database with additional data to improve its accuracy.
Additionally, it is worth noting that the study examined a small number of modified mouse models and tumoroids, which could have biased the results.
While CCN is still in its infancy, it already demonstrates considerable potential in terms of assisting researchers in determining how realistic their cancer models are – and how dependable studies based on them will be when it comes to translating them into actual treatments. Additionally, it is easily adaptable to future cancer models.
“Because CCN is open-source and easy to use, it can be readily applied to newly generated cancer models as a means to assess their fidelity,” the researchers write in their report.
The study was published in the journal Genome Medicine.