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Prediction of toxicant-specific gene expression signatures following chemotherapeutic treatment of breast cell lines

ABSTRACT: Global gene expression profiling has demonstrated that the predominant cellular response to a range of toxicants is a general stress response. This stereotyped environmental stress response commonly includes repression of protein synthesis and cell cycle regulated genes and induction of DNA damage and oxidative stress responsive genes. Our laboratory has recently characterized the general stress response of breast cell lines derived from basal-like and luminal epithelium following treatment with doxorubicin (DOX) or 5-fluorouracil (5FU) and showed that each cell type has a distinct response. However, we expected that some of the expression changes induced by DOX and 5FU were unique to each compound and might reflect the underlying mechanisms of action of these agents. Thus, we employed supervised analyses (Significance Analysis of Microarrays) to identify genes that showed differential expression between DOX- and 5FU-treated cell lines. We then used cross-validation analyses and identified genes that afforded high predictive accuracy in classifying samples into the two treatment classes. To test whether these gene lists had good predictive accuracy in an independent data set, we treated our panel of cell lines with etoposide, a compound that is mechanistically similar to DOX. We demonstrated that using expression patterns of 100 genes, we were able to obtain 100% predictive accuracy in classifying the etoposide samples as being more similar in expression to DOX- than 5FU-treated samples. These analyses also showed that toxicant specific gene expression patterns, similar to general stress responses, vary according to cell type. Keywords = breast cancer Keywords = class prediction Keywords = doxorubicin Keywords = etoposide Keywords = 5-fluorouricil Keywords = gene expression Keywords = microarrays Keywords: dose response

ORGANISM(S): Homo sapiens

PROVIDER: GSE1647 | GEO | 2004/09/28

SECONDARY ACCESSION(S): PRJNA90701

REPOSITORIES: GEO

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Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification

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Prediction of toxicant-specific gene expression signatures following chemotherapeutic treatment of breast cell lines

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