The influence of tumor size and environment on gene expression in commonly used human tumor lines

BACKGROUND: The expression profiles of solid tumor models in rodents have been only minimally studied despite their extensive use to develop anticancer agents. We have applied RNA expression profiling using Affymetrix U95A GeneChips to address fundamental biological questions about human tumor lines. METHODS: To determine whether gene expression changed significantly as a tumor increased in size, we analyzed samples from two human colon carcinoma lines (Colo205 and HCT-116) at three different sizes (200 mg, 500 mg and 1000 mg). To investigate whether gene expression was influenced by the strain of mouse, tumor samples isolated from C.B-17 SCID and Nu/Nu mice were also compared. Finally, the gene expression differences between tissue culture and in vivo samples were investigated by comparing profiles from lines grown in both environments. RESULTS: Multidimensional scaling and analysis of variance demonstrated that the tumor lines were dramatically different from each other and that gene expression remained constant as the tumors increased in size. Statistical analysis revealed that 63 genes were differentially expressed due to the strain of mouse the tumor was grown in but the function of the encoded proteins did not link to any distinct biological pathways. Hierarchical clustering of tissue culture and xenograft samples demonstrated that for each individual tumor line, the in vivo and in vitro profiles were more similar to each other than any other profile. We identified 36 genes with a pattern of high expression in xenograft samples that encoded proteins involved in extracellular matrix, cell surface receptors and transcription factors. An additional 17 genes were identified with a pattern of high expression in tissue culture samples and encoded proteins involved in cell division, cell cycle and RNA production. CONCLUSIONS: The environment a tumor line is grown in can have a significant effect on gene expression but tumor size has little or no effect for subcutaneously grown solid tumors. Furthermore, an individual tumor line has an RNA expression pattern that clearly defines it from other lines even when grown in different environments. This could be used as a quality control tool for preclinical oncology studies

Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

Hierarchical clustering analysis showing the structure within the data of the 13 tissue culture samples (suffix – TC) and 8 xenograft samples (suffix – X)

<p><b>Copyright information:</b></p><p>Taken from "The influence of tumor size and environment on gene expression in commonly used human tumor lines"</p><p>BMC Cancer 2004;4():35-35.</p><p>Published online 15 Jul 2004</p><p>PMCID:PMC493269.</p><p>Copyright © 2004 Gieseg et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.</p> Samples are displayed vertically, genes are displayed horizontally. A dendrogram of relatedness of the samples is at the top in green. For any two samples, the vertical distance from the sample roots to the first node joining them is a measure of their similarity; the shorter the distance the more similar. The color in each cell of the table represents the median adjusted expression value of each gene. The color scale used to represent the expression ratios is shown on the right, with yellow indicating increased expression relative to the median and blue decreased

Differential Utilization and Localization of ErbB Receptor Tyrosine Kinases in Skin Compared to Normal and Malignant Keratinocytes

Induction of heparin-binding epidermal growth factor-like growth factor (HB-EGF) mRNA in mouse skin organ culture was blocked by two pan-ErbB receptor tyrosine kinase (RTK) inhibitors but not by genetic ablation of ErbB1, suggesting involvement of multiple ErbB species in skin physiology. Human skin, cultured normal keratinocytes, and A431 skin carcinoma cells expressed ErbB1, ErbB2, and ErbB3, but not ErbB4. Skin and A431 cells expressed more ErbB3 than did keratinocytes. Despite strong expression of ErbB2 and ErbB3, heregulin was inactive in stimulating tyrosine phosphorylation in A431 cells. In contrast, it was highly active in MDA-MB-453 breast carcinoma cells. ErbB2 displayed punctate cytoplasmic staining in A431 and keratinocytes, compared to strong cell surface staining in MDA-MB-453. In skin, ErbB2 was cytoplasmic in basal keratinocytes, assuming a cell surface pattern in the upper suprabasal layers. In contrast, ErbB1 retained a cell surface distribution in all epidermal layers. Keratinocyte proliferation in culture was found to be ErbB1-RTK-dependent, using a selective inhibitor. These results suggest that in skin keratinocytes, ErbB2 transduces ligand-dependent differentiation signals, whereas ErbB1 transduces ligand-dependent proliferation/survival signals. Intracellular sequestration of ErbB2 may contribute to the malignant phenotype of A431 cells, by allowing them to respond to ErbB1-dependent growth/survival signals, while evading ErbB2-dependent differentiation signals