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"Off-context" gene expression in lung cancer identifies a group of metastatic-prone tumors

ABSTRACT: An unexplored consequence of epigenetic alterations associated with cancer is the ectopic expression of tissue-restricted genes. Here, a new strategy was developed to decipher genome-wide expression data in search for these “off-context” gene activations, which consisted first, in identifying a large number of tissue-specific genes normally epigenetically silenced in most somatic cells and second, in using them as cancer biomarkers on an “on/off” basis. Applying this concept to analyze whole-genome transcriptome data in lung cancer, we discovered a specific group of 26 genes whose expression was a strong and independent predictor of poor prognosis in our cohort of 293 lung tumours, as well as in two independent external populations. In addition, these 26 classifying genes enabled us to isolate a homogenous group of metastatic-prone highly aggressive tumours, whose characteristic gene expression profile revealed a high proliferative potential combined to a significant decrease in immune and signaling functions. This work illustrates a new approach for a personalized management of cancer, with applications to any cancer type.

ORGANISM(S): Homo sapiens

PROVIDER: GSE30219 | GEO | 2013/05/24

SECONDARY ACCESSION(S): PRJNA143861

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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"Off-context" gene expression in lung cancer identifies a group of metastatic-prone tumors

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