Combined analysis of gene expression, DNA copy number, and mutation profiling data to display biological process anomalies in individual breast cancers.

Abstract

The goal of this analysis was to develop a computational tool that integrates the totality of gene expression, DNA copy number, and sequence abnormalities in individual cancers in the framework of biological processes. We used the hierarchical structure of the gene ontology (GO) database to create a reference network and projected mRNA expression, DNA copy number and mutation anomalies detected in single samples into this space. We applied our method to 59 breast cancers where all three types of molecular data were available. Each cancer had a large number of disturbed biological processes. Locomotion, multicellular organismal process, and signal transduction pathways were the most commonly affected GO terms, but the individual molecular events were different from case-to-case. Estrogen receptor-positive and -negative cancers had different repertoire of anomalies. We tested the functional impact of 27 mRNAs that had overexpression in cancer with variable frequency (<2-42 %) using an siRNA screen. Each of these genes inhibited cell growth in at least some of 18 breast cancer cell lines. We developed a free, on-line software tool ( http://netgoplot.org ) to display the complex genomic abnormalities in individual cancers in the biological framework of the GO biological processes. Each cancer harbored a variable number of pathway anomalies and the individual molecular events that caused an anomaly varied from case-to-case. Our in vitro experiments indicate that rare case-specific molecular abnormalities can play a functional role and driver events may vary from case-to-case depending on the constellation of other molecular anomalies.

• Authors: Weiwei Shi, Balint Balazs, Balazs Györffy, Tingting Jiang, W Fraser Symmans, Christos Hatzis, Lajos Pusztai
• Journal: Breast cancer research and treatment (Breast Cancer Res Treat) Vol. 144 Issue 3 Pg. 561-8 (Apr 2014) ISSN: 1573-7217 [Electronic] Netherlands
• PMID: 24619174 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Breast Neoplasms (genetics, pathology)
• Cell Line, Tumor
• Cluster Analysis
• Computational Biology (methods)
• DNA Copy Number Variations
• Data Mining
• Databases, Nucleic Acid
• Female
• Gene Expression Regulation, Neoplastic
• Gene Regulatory Networks
• Genomics (methods)
• Humans
• Molecular Sequence Annotation
• Mutation
• Reproducibility of Results