August 10, 2022

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Why did so many cancer cell experiments fail and waste huge money?

Why did so many cancer cell experiments fail and waste huge money?

Why did so many cancer cell experiments fail and waste huge money?  Why did many cancer cell experiments always get wrong way? Cancer cells in a petri dish may be quite different from cancer cells in the human body. 

Cancer has always been one of the biggest nightmares in the history of human diseases. It is also known as the “king of all diseases” because of its complexity and incurable nature. Even in today’s highly developed medical technology, when it comes to cancer, everyone naturally associates it with death, especially advanced cancer.

In order to find a cure for cancer, scientists often cultivate various cancer cell lines in a laboratory environment and test the killing effects of different drugs on these cancer cell lines. It is worth noting that many scientific researchers obtain cancer cell lines through different channels, but few people verify whether these cancer cell lines are correct and whether they have been confused.

Recently, researchers from Johns Hopkins University School of Medicine in the United States published a research paper entitled: Evaluating the transcriptional fidelity of cancer models in the journal Genome Medicine.

Based on the idea of ​​finding or improving research models in cancer laboratories, this research developed a new, self-learning computer model—CancerCellNet to measure the similarity between different cancer research models and native tumors. Research results show that among several commonly used tumor research models, human cancer cell lines grown in petri dishes are genetically the least similar to cancer cells in humans. Genetically engineered mice and 3D tumor models are similar to those in humans. Cancer cells have a higher similarity.

Why did so many cancer cell experiments fail and waste huge money?

The construction of cancer models is an important way to study cancer biology and identify potential treatment methods. Among them, the most popular modeling methods are cancer cell lines (CCLs), genetically engineered mouse models (GEMMs), and human tumor xenograft models (PDXs). ) And 3D tumor models (tumoroids).

The generalization and ability of the model come from the fidelity of the tumor type it represents. Therefore, no matter what kind of model, in principle, it should be as close as possible to the corresponding cancer type in the patient. Unfortunately, many researchers are not clear about the similarities between different cancer models and corresponding native tumors.

In this study, the research team developed a computer model based on machine learning, CancerCellNet, using RNA in cells as the research object.

Corresponding author Patrick Cahan said that RNA is a very good substitute for cell type and cell identity, which is the key to determining whether cells grown in the laboratory are similar to human cells. The RNA expression data are very standardized and are not easily affected by mutations that confuse the results of the study.

Why did so many cancer cell experiments fail and waste huge money?CancerCellNet is a self-learning computer model based on RNA information

In order to build CancerCellNet, the research team must select a set of standard data as the baseline for the comparative research model. In this regard, they obtained relevant data from the TCGA database, which contained the RNA expression information of hundreds of patient tumor samples, as well as the corresponding tumor information such as staging and grading.

Subsequently, the researchers tested the CancerCellNet tool and applied it to data of known tumor types, such as data from the International Human Genome Sequencing Consortium.

Why did so many cancer cell experiments fail and waste huge money?Construction, testing and training of CancerCellNet tools

The research team combed through the data of the cancer genome map and identified 22 tumor types available for research. They used genomic profile data as a baseline for comparing RNA expression data, which came from 657 cancer cell lines, 415 xenografts, 26 genetically engineered mouse models, and 131 tumor-like models cultivated in laboratories around the world.

Not only that, the research team also used the 0-1 scoring method to evaluate the similarity between different tumor models and native tumors. The researchers found that the average score of cancer cell lines was lower than that of xenografts, genetically engineered mouse models, and tumor-like models.

Corresponding author Patrick Cahan said that perhaps it is not surprising that cancer cell lines are genetically inferior to other models. Due to the complex differences in growth environment, cancer cell lines grown in the laboratory are not the same as human-derived cancer cells. In other words, once the tumor is removed from its natural environment, the cell line has already begun to change!

Scoring of different cancer research models using the CancerCellNet tool

For example, the research team found that prostate cancer cells from PC3 began to look more like bladder cancer genetically. It is also possible that the cell line was initially labeled incorrectly, or that the cell line was actually extracted from bladder cancer. But in any case, only from a genetic point of view, this prostate cancer cell line does not represent a typical prostate cancer patient.

Genetically engineered mice and tumor-like tumors have higher transcription fidelity than patient-derived xenografts and cancer cell lines

All in all, the research developed the CancerCellNet tool to record cancer models with the highest transcriptional fidelity to natural tumors, and found cancer models that did not match the annotated classification. By comparing models of different models, among several commonly used tumor types, genetically engineered mice and 3D tumor models have higher transcription fidelity than human xenograft models and cancer cell lines.

Therefore, researchers must choose carefully before applying cancer models. This may be the key to determining the success or failure of your experiment!

(source:internet, reference only)

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