BCAR1, a human homologue of the adapter protein p130Cas, and antiestrogen resistance in breast cancer cells

BACKGROUND: Treatment of breast cancer with the antiestrogen tamoxifen is effective in approximately one half of the patients with estrogen receptor-positive disease, but tumors recur frequently because of the development of metastases that are resistant to tamoxifen. We have previously shown that mutagenesis of human estrogen-dependent ZR-75-1 breast cancer cells by insertion of a defective retrovirus genome caused the cells to become antiestrogen resistant. In this study, we isolated and characterized the crucial gene at the breast cancer antiestrogen resistance 1 (BCAR1) locus. METHODS/RESULTS: Transfer of the BCAR1 locus from retrovirus-mutated, antiestrogen-resistant cells to estrogen-dependent ZR-75-1 cells by cell fusion conferred an antiestrogen-resistant phenotype on the recipient cells. The complete coding sequence of BCAR1 was isolated by use of exon-trapping and complementary DNA (cDNA) library screening. Sequence analysis of human BCAR1 cDNA predicted a protein of 870 amino acids that was strongly homologous to rat p130Cas-adapter protein. Genomic analysis revealed that BCAR1 consists of seven exons and is located at chromosome 16q23.1. BCAR1 transcripts were detected in multiple human tissues and were similar in size to transcripts produced by retrovirus-mutated ZR-75-1 cells. Transfection of BCAR1 cDNA into ZR-75-1 cells again resulted in sustained cell proliferation in the presence of antiestrogens, confirming that BCAR1 was the responsible gene in the locus. CONCLUSIONS: Overexpression of the BCAR1 gene confers antiestrogen resistance on human ZR-75-1 breast cancer cells. Overexpression of BCAR1 in retrovirus-mutated cells appears to result from activation of the gene's promoter. The isolation and characterization of this gene open new avenues to elucidating mechanisms by which the growth of human breast cancer becomes independent of estrogen

DMRforPairs: Identifying Differentially Methylated Regions between unique samples using array based methylation profiles

Background: Array based methylation profiling is a cost-effective solution to study the association between genome methylation and human disease & development. Available tools to analyze the Illumina Infinium HumanMethylation450 BeadChip focus on comparing methylation levels per locus. Other tools combine multiple probes into a range, identifying differential methylated regions (DMRs). These tools all require groups of samples to compare. However, comparison of unique, individual samples is essential in situations where larger sample sizes are not possible.Results: DMRforPairs was designed to compare regional methylation status between unique samples. It identifies probe dense genomic regions and quantifies/tests their (difference in) methylation level between the samples. As a proof of concept, DMRforPairs is applied to public data from four human cell lines: two lymphoblastoid cell lines from healthy individuals and the cancer cell lines A431 and MCF7 (including 2 technical replicates each). DMRforPairs identified an increasing number of DMRs related to the sample phenotype when biological similarity of the samples decreased. DMRs identified by DMRforPairs were related to the biological origin of the cell lines.Conclusion: To our knowledge, DMRforPairs is the first tool to identify and visualize relevant and significant differentially methylated regions between unique samples

Anni 2.0: a multipurpose text-mining tool for the life sciences

Anni 2.0 provides an ontology-based interface to MEDLINE

Pleiotropic actions of suramin on the proliferation of human breast-cancer cells in vitro

Suramin, a non‐specific growth factor antagonist, is currently under investigation for treatment of cancer patients. We studied its action on 6 different human breast‐cancer cell lines in vitro. In complete growth medium, pleiotropic effects were observed with respect to cell proliferation, i.e. suramin is stimulatory at low concentrations and inhibitory at higher concentrations, for 4 of the 6 cell lines studied. The various cell lines showed marked differences with respect to the antiproliferative action of suramin, the Evsa‐T cells being by far the most sensitive ones. A suramin concentration of 100 μg/ml brought about a 100% stimulation of the proliferation of ZR/HERc cells, ZR 75.1 cells ectopically expressing a human epidermal growth factor receptor (EGF‐R) cDNA. Although less pronounced (10 to 60% stimulation), a similar response was observed for the parent ZR 75.1 cells, as well as for T‐47D and MDA‐MB‐231 cells. The non‐specificity of the action of suramin was established by the observation that suramin‐induced inhibition of cell proliferation could be abolished by insulin‐like growth factor‐1 (IGF‐I) or basic fibroblast growth factor (bFGF), and even by estradiol, both in complete growth medium and under defined serum‐free conditions. Our data indicate that suramin exerts pleiotropic effects on the proliferation of human breast cancer cells in vitro, and confirm the non‐specific nature of its action. The stimulatory effect of low concentrations of suramin on the proliferation of breast cancer cells may have important consequences for breast cancer patients treated with suramin. Copyrigh

Mining microarray datasets aided by knowledge stored in literature

DNA microarray technology produces large amounts of data. For data mining of these datasets, background information on genes can be helpful. Unfortunately most information is stored in free text. Here, we present an approach to use this information for DNA microarray data mining

BCAR4 induces antioestrogen resistance but sensitises breast cancer to lapatinib

BACKGROUND: High BCAR4 and ERBB2 mRNA levels in primary breast cancer associate with tamoxifen resistance and poor patient outcome. We determined whether BCAR4 expression sensitises breast cancer cells to lapatinib, and identifies a subgroup of patients who possibly may benefit from ERBB2-targeted therapies despite having tumours with low ERBB2 expression. METHODS: Proliferation assays were applied to determine the effect of BCAR4 expression on lapatinib treatment. Changes in cell signalling were quantified with reverse-phase protein microarrays. Quantitative reverse-transcriptase polymerase chain reaction (RT-PCR) of ERBB2 and BCAR4 was performed in 1418 primary breast cancers. Combined BCAR4 and ERBB2 mRNA levels were evaluated for association with progression-free survival (PFS) in 293 oestrogen receptor-alpha (ER)-positive patients receiving t RESULTS: BCAR4 expression strongly sensitised ZR-75-1 and MCF7 breast cancer cells to the combination of lapatinib and antioestrogens. Lapatinib interfered with phosphorylation of ERBB2 and its downstream mediators AKT, FAK, SHC, STAT5, and STAT6. Reverse transcriptase-PCR analysis showed that 27.6% of the breast cancers were positive for BCAR4 and 22% expressed also low levels of ERBB2. The clinical significance of combining BCAR4 and ERBB2 mRNA status was underscored by the finding that the gr CONCLUSION: This study shows that BCAR4 expression identifies a subgroup of ER-positive breast cancer patients without overexpression of ERBB2 who have a poor outcome and might benefit from combined ERBB2-targeted and antioestrogen therapy. British Journal of Cancer (2012) 107, 947-955. doi:10.1038/bjc.2012.351 www.bjcancer.com Published online 14 August 2012 (C) 2012 Cancer Research U

Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes

MOTIVATION: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method. RESULTS: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes. AVAILABILITY: The ACS program is available upon request from the authors