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Breast Cancer Research and Treatment

, Volume 144, Issue 3, pp 561–568 | Cite as

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

  • Weiwei Shi
  • Balint Balazs
  • Balazs Györffy
  • Tingting Jiang
  • W. Fraser Symmans
  • Christos Hatzis
  • Lajos Pusztai
Preclinical study

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.

Keywords

Software tool Integrated omics analysis Systems biology Biological networks 

Notes

Acknowledgements

This work was supported in part by The Breast Cancer Research Foundation (LP) and from OTKA PD83154 (BG).

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Weiwei Shi
    • 1
  • Balint Balazs
    • 2
  • Balazs Györffy
    • 2
  • Tingting Jiang
    • 1
  • W. Fraser Symmans
    • 3
  • Christos Hatzis
    • 1
  • Lajos Pusztai
    • 1
  1. 1.Yale Cancer Center, Yale School of MedicineYale UniversityNew HavenUSA
  2. 2.Research Laboratory for Pediatrics and NephrologyThe Hungarian Academy of SciencesBudapestHungary
  3. 3.Department of Pathology, M. D. Anderson Cancer CenterThe University of TexasHoustonUSA

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