Dataset Information

Cancer Cell Acid Adaptation Gene Expression Response Is Correlated to Tumor-Specific Tissue Expression Profiles and Patient Survival.

ABSTRACT: The acidic pH of the tumor microenvironment plays a critical role in driving cancer development toward a more aggressive phenotype, but the underlying mechanisms are unclear. To this end, phenotypic and genotypic changes induced by adaptation of cancer cells to chronic acidosis have been studied. However, the generality of acid adaptation patterns across cell models and their correlation to the molecular phenotypes and aggressiveness of human cancers are essentially unknown. Here, we define an acid adaptation expression response shared across three cancer cell models, dominated by metabolic rewiring, extracellular matrix remodeling, and altered cell cycle regulation and DNA damage response. We find that many genes which are upregulated by acid adaptation are significantly correlated to patient survival, and more generally, that there are clear correlations between acid adaptation expression response and gene expression change between normal and tumor tissues, for a large subset of cancer patients. Our data support the notion that tumor microenvironment acidity is one of the key factors driving the selection of aggressive cancer cells in human patient tumors, yet it also induces a growth-limiting genotype that likely limits cancer cell growth until the cells are released from acidosis, for instance during invasion.

SUBMITTER: Yao J

PROVIDER: S-EPMC7463722 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Cancer Cell Acid Adaptation Gene Expression Response Is Correlated to Tumor-Specific Tissue Expression Profiles and Patient Survival.

Yao Jiayi J Czaplinska Dominika D Ialchina Renata R Schnipper Julie J Liu Bin B Sandelin Albin A Pedersen Stine Falsig SF

Cancers 20200805 8

The acidic pH of the tumor microenvironment plays a critical role in driving cancer development toward a more aggressive phenotype, but the underlying mechanisms are unclear. To this end, phenotypic and genotypic changes induced by adaptation of cancer cells to chronic acidosis have been studied. However, the generality of acid adaptation patterns across cell models and their correlation to the molecular phenotypes and aggressiveness of human cancers are essentially unknown. Here, we define an a ...[more]

PMID: 32764426

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Project description: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.

Project description:Preclinical 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. Here 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 by using publicly available gene expression profiles across multiple sources. We found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that 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. Future 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.

Dataset Information

Cancer Cell Acid Adaptation Gene Expression Response Is Correlated to Tumor-Specific Tissue Expression Profiles and Patient Survival.

Publications

Cancer Cell Acid Adaptation Gene Expression Response Is Correlated to Tumor-Specific Tissue Expression Profiles and Patient Survival.

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