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


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 ( 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.


Software tool Integrated omics analysis Systems biology Biological networks 



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

10549_2014_2904_MOESM1_ESM.docx (14 kb)
Supplementary material 1 (DOCX 15 kb)
10549_2014_2904_MOESM2_ESM.pdf (19 kb)
Supplementary material 2 (PDF 20 kb)
10549_2014_2904_MOESM3_ESM.pdf (216 kb)
Supplementary material 3 (PDF 217 kb)
10549_2014_2904_MOESM4_ESM.pdf (216 kb)
Supplementary material 4 (PDF 217 kb)
10549_2014_2904_MOESM5_ESM.pdf (216 kb)
Supplementary material 5 (PDF 217 kb)
10549_2014_2904_MOESM6_ESM.pdf (216 kb)
Supplementary material 6 (PDF 216 kb)
10549_2014_2904_MOESM7_ESM.pdf (16 kb)
Supplementary material 7 (PDF 17 kb)
10549_2014_2904_MOESM8_ESM.pdf (1.5 mb)
Supplementary material 8 (PDF 1538 kb)
10549_2014_2904_MOESM9_ESM.xlsx (22 kb)
Supplementary material 9 (XLSX 23 kb)
10549_2014_2904_MOESM10_ESM.xls (34 kb)
Supplementary material 10 (XLS 35 kb)
10549_2014_2904_MOESM11_ESM.xlsx (12 kb)
Supplementary material 11 (XLSX 12 kb)
10549_2014_2904_MOESM12_ESM.xls (35 kb)
Supplementary material 12 (XLS 35 kb)
10549_2014_2904_MOESM13_ESM.xlsx (112 kb)
Supplementary material 13 (XLSX 113 kb)


  1. 1.
    Cao Y, DePinho RA, Ernst M, Vousden K (2011) Cancer research: past, present and future. Nat Rev Cancer 11:749–754PubMedCrossRefGoogle Scholar
  2. 2.
    Beroukhim R et al (2010) The landscape of somatic copy-number alteration across human cancers. Nature 463:899–905PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Thomas RK et al (2007) High-throughput oncogene mutation profiling in human cancer. Nat Genet 39:347–351PubMedCrossRefGoogle Scholar
  4. 4.
    Stratton MR, Campbell PJ, Futreal A (2009) The Cancer Genome. Nature 458:719–724PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Greenman C et al (2007) Patterns of somatic mutation in human cancer genomes. Nature 446:153–158PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Vaske CJ et al (2010) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26:237–245CrossRefGoogle Scholar
  7. 7.
    Kamada T, Kawai S (1989) An algorithm for drawing general undirected graphs. Inf Process Lett (Elsevier) 31:7–15CrossRefGoogle Scholar
  8. 8.
    Olshen AB, Venkatraman ES, Lucito R, Wigler M (2004) Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5:557–572PubMedCrossRefGoogle Scholar
  9. 9.
    Boca SM et al (2010) Patient-oriented gene set analysis for cancer mutation data. Genome Biol 11:R112PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Iwamoto T, Bianchini G, Booser D, Qi Y et al (2011) Gene pathways associated with prognosis and chemotherapy sensitivity in molecular subtypes of breast cancer. J Natl Cancer Inst 103(3):264–272PubMedCrossRefGoogle Scholar
  11. 11.
    Andre F et al (2009) Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array. Clin Cancer Res 15:441–451PubMedCrossRefGoogle Scholar
  12. 12.
    Nekrutenko A, Taylor J (2012) Next-generation sequencing data interpretation: enhancing reproducibility and accessibility. Nat Rev Genet 13:667–672Google Scholar
  13. 13.
    Wang K, Li M, Hakonarson H (2010) Annovar: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38(16):e164PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Reva B, Antipin Y, Sander C (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 39(17):e118PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Ng PC, Henikoff S (2006) Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet 7:61–80PubMedCrossRefGoogle Scholar
  16. 16.
    Ng PC, Henikoff S (2003) Sift: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Torkamani A, Schork NJ (2008) Prediction of cancer driver mutations in protein kinases. Cancer Res 68:1675–1682PubMedCrossRefGoogle Scholar
  18. 18.
    Torkamani A, Schork NJ (2007) Accurate prediction of deleterious protein kinase polymorphisms. Bioinformatics 23:2918–2925PubMedCrossRefGoogle Scholar

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

Personalised recommendations