Evaluating cancer cell line and patient‐derived xenograft recapitulation of tumor and non‐diseased tissue gene expression profiles in silico

Abstract

BackgroundPreclinical models like cancer cell lines and patient‐derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have demonstrated that preclinical models do not recapitulate all biological aspects of human tissues, particularly with respect to the tissue of origin gene expression signatures. Therefore, it is critical to assess how well preclinical model gene expression profiles correlate with human cancer tissues to inform preclinical model selection and data analysis decisions.AimsHere we evaluated how well preclinical models recapitulate human cancer and non‐diseased tissue gene expression patterns in silico with respect to the full gene expression profile as well as subsetting by the most variable genes, genes significantly correlated with tumor purity, and tissue‐specific genes.MethodsBy using publicly available gene expression profiles across multiple sources, we evaluated cancer cell line and patient‐derived xenograft recapitulation of tumor and non‐diseased tissue gene expression profiles in silico.ResultsWe found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that glioblastoma (GBM) PDX global gene expression correlation to GBM tumor global gene expression outperforms GBM cell line to GBM tumor global gene expression correlations, and demonstrated that preclinical models in our study often failed to reproduce tissue‐specific expression. While including additional genes for global gene expression comparison between cell lines and tissues decreases the overall correlation, it improves the relative rank between a cell line and its tissue of origin compared to other tissues. Our findings underscore the importance of using the full gene expression set measured when comparing preclinical models and tissues and confirm that tissue‐specific patterns are better preserved in GBM PDX models than in GBM cell lines.ConclusionFuture studies can build on these findings to determine the specific pathways and gene sets recapitulated by particular preclinical models to facilitate model selection for a given study design or goal.

Implications of ZNF334 gene in lymph node metastasis of lung SCC: potential bypassing of cellular senescence

“…Although cancer cell lines, contrary to tissue samples that are heterogenous and, through bearing an in vivo microenvironment, have a better match with the patient’s tumor profile, are often thought to be homogenous and fail to maintain the heterogeneity of the original tumor profile, the employment of these human cancer-derived cell lines as in vitro models for studying cancer biology and testing hypotheses in translational research would be valuable as long as they have equal value or relevance as tumor models [ 21 , 22 ]. In this work, we recruited a rarely used and very low-passaged lung SCC cell lines from the Pasteur Institute of Iran-North Research Center branch (The Pasteur Institute of Amol) that had a closer resemblance to the original tumor profiles than the commonly used same cell line of the Pasteur Institute of Iran located in Tehran, which has grown for too many passages.…”