Abstract
Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.
Highlights
Breast cancer is one of the most common malignancies in the world
Using the cyclin B1 (CCNB1) gene and a Core Serum Response gene signature as two examples we illustrate the usefulness of Gene expression-based Outcome for Breast cancer Online (GOBO) for rapid online analysis in a large breast cancer data set Overview of GOBO applications The web interface of GOBO allows precompiled data sets to be queried by the three main applications of GOBO: Gene Set Analysis (GSA), Co-expressed Genes (CG), and Sample Prediction (SP) (Figure 1)
The precompiled data sets consist of gene expression data and annotation data for a pooled 1881-sample breast tumor set and 51 previously reported breast cancer cell lines [23]
Summary
Breast cancer is one of the most common malignancies in the world. It is evident that breast cancer is a heterogeneous disease characterized by different tumor biological and histological subtypes, age of onset, clinical course and responsiveness to treatment. Microarray-based gene expression profiling of breast carcinoma holds promise for future improvement in treatment decisions, current studies aimed at deriving prognostic or treatment predictive gene signatures are often limited by a low number of included patients, typically tens to hundreds, due to cost and availability of suitable tissue specimens. The growing accumulation of data from prognostic and/or treatment predictive gene expression studies on breast cancer in public repositories, e.g. Gene Expression Omnibus [13], provides an opportunity to construct pooled gene expression data sets comprising a larger number of patients with sufficient clinical data. Data sets in larger pooled sets are often individually available through public repositories, the combined pooled data sets are commonly not available, thereby representing an obstacle to researchers with limited bioinformatic resources To address this latter shortcoming, we developed Gene expression-based Outcome for Breast cancer Online (GOBO). Using the CCNB1 gene and a Core Serum Response gene signature as two examples we illustrate the usefulness of GOBO for rapid online analysis in a large breast cancer data set
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