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Updated: Jul 6, 2026
A Melanoma Patient-Derived Xenograft Model
Published on: May 20, 2019
Global comparative gene expression analysis of melanoma patient samples, derived cell lines and corresponding tumor
Yaguang Xi1, Adam Riker, Lalita Shevde-Samant
1Mitchell Cancer Institute, Mobile, AL 36688, USA.
Abstract:
Various in vitro and in vivo experimental models have been used for the discovery of genes and pathways involved in melanoma and other types of cancer. However, in many cases, the results from various tumor models failed to be validated successfully in clinical studies. Limited information is available on how closely these models reflect the in vivo physiological conditions. In this study, a comprehensive genomics approach was used to systematically compare the expression patterns of snap frozen samples obtained from patients with primary melanoma, lymph node metastasis, and distant metastases, and compare these patterns to those of their corresponding cell lines and tumor xenografts in nude mice. The GE Healthcare 20k human genome array was used and the expression data was normalized and analyzed using GeneSpring 7.2 software. Based on the expression analysis, the correlation rate between the snap frozen primary patient samples vs. derived cell lines was 66%, with 1687 differentially expressed genes. The correlation rate between the snap frozen primary patient samples and the tumor xenografts was 75%, with 1,374 differentially expressed genes, and the correlation rate comparing tumor xenografts to derived cell lines ranged between 58% and 84%. These results demonstrated significant gene expression differences between tumor materials with different in vitro and in vivo growth microenvironments. Such studies can help us to distinguish between genes up- or down-regulated as a result of the microenvironment and those stably expressed independently of the tumor milieu. With the extensive use of cell lines and xenografts in cancer research, the information obtained using our approach may help to better interpret results generated from different tumor models by understanding common differences, as well as similarities at the gene expression level, information that may have important practical and biological implications.
