Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

An integrated genomics approach identifies drivers of proliferation in luminal-subtype human breast cancer

Nature Genetics volume 46, pages 1051–1059 (2014)Cite this article

Subjects

Abstract

Elucidating the molecular drivers of human breast cancers requires a strategy that is capable of integrating multiple forms of data and an ability to interpret the functional consequences of a given genetic aberration. Here we present an integrated genomic strategy based on the use of gene expression signatures of oncogenic pathway activity (n = 52) as a framework to analyze DNA copy number alterations in combination with data from a genome-wide RNA-mediated interference screen. We identify specific DNA amplifications and essential genes within these amplicons representing key genetic drivers, including known and new regulators of oncogenesis. The genes identified include eight that are essential for cell proliferation (FGD5, METTL6, CPT1A, DTX3, MRPS23, EIF2S2, EIF6 and SLC2A10) and are uniquely amplified in patients with highly proliferative luminal breast tumors, a clinical subset of patients for which few therapeutic options are effective. This general strategy has the potential to identify therapeutic targets within amplicons through an integrated use of genomic data sets.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Patterns of genomic signature pathway activity in breast cancer.
Figure 2: Identification of genomic pathway–specific CNAs.
Figure 3: Identification of DNA CNAs in highly proliferative breast tumors.
Figure 4: Identification of genomic pathway–associated essential genes in cell lines.
Figure 5: Identification of essential genes amplified in highly proliferative luminal tumors.
Figure 6: Candidate gene amplification correlates with a poor prognosis.

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Perou, C.M. et al. Molecular portraits of human breast tumors. Nature 406, 747–752 (2000).

    Article  CAS  Google Scholar 

  2. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  3. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

    Article  CAS  Google Scholar 

  4. Wood, L.D. et al. The genomic landscapes of human breast and colorectal cancers. Science 318, 1108–1113 (2007).

    Article  CAS  Google Scholar 

  5. Bild, A.H. et al. An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer. Breast Cancer Res. 11, R55 (2009).

    Article  Google Scholar 

  6. Bild, A.H. et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439, 353–357 (2006).

    Article  CAS  Google Scholar 

  7. Rhodes, D.R. et al. Molecular concepts analysis links tumors, pathways, mechanisms, and drugs. Neoplasia 9, 443–454 (2007).

    Article  CAS  Google Scholar 

  8. Vogelstein, B. & Kinzler, K.W. Cancer genes and the pathways they control. Nat. Med. 10, 789–799 (2004).

    Article  CAS  Google Scholar 

  9. Marcotte, R. et al. Essential gene profiles in breast, pancreatic, and ovarian cancer cells. Cancer Discov. 2, 172–189 (2012).

    Article  CAS  Google Scholar 

  10. Gatza, M.L. et al. Analysis of tumor environmental response and oncogenic pathway activation identifies distinct basal and luminal features in HER2-related breast tumor subtypes. Breast Cancer Res. 13, R62 (2011).

    Article  Google Scholar 

  11. Gatza, M.L. et al. A pathway-based classification of human breast cancer. Proc. Natl. Acad. Sci. USA 107, 6994–6999 (2010).

    Article  CAS  Google Scholar 

  12. Fan, C. et al. Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures. BMC Med. Genomics 4, 3 (2011).

    Article  Google Scholar 

  13. Hoadley, K.A. et al. EGFR associated expression profiles vary with breast tumor subtype. BMC Genomics 8, 258 (2007).

    Article  Google Scholar 

  14. Troester, M.A. et al. Gene expression patterns associated with p53 status in beast cancer. BMC Cancer 6, 276 (2006).

    Article  Google Scholar 

  15. Chandriani, S. et al. A core MYC gene expression signature is prominent in basal-like breast cancer but only partially overlaps the core serum response. PLoS ONE 4, e6693 (2009).

    Article  Google Scholar 

  16. Herschkowitz, J.I., He, X., Fan, C. & Perou, C.M. The functional loss of the retinoblastoma tumour suppressor is a common event in basal-like and luminal B breast carcinomas. Breast Cancer Res. 10, R75 (2008).

    Article  Google Scholar 

  17. Hu, Z. et al. A compact VEGF signature associated with distant metastases and poor outcomes. BMC Med. 7, 9 (2009).

    Article  Google Scholar 

  18. Hutti, J.E. et al. Oncogenic PI3K mutations lead to NF-κB–dependent cytokine expression following growth factor deprivation. Cancer Res. 72, 3260–3269 (2012).

    Article  CAS  Google Scholar 

  19. Oh, D.S. et al. Estrogen-regulated genes predict survival in hormone receptor–positive breast cancers. J. Clin. Oncol. 24, 1656–1664 (2006).

    Article  CAS  Google Scholar 

  20. Thorner, A.R. et al. In vitro and in vivo analysis of B-Myb in basal-like breast cancer. Oncogene 28, 742–751 (2009).

    Article  CAS  Google Scholar 

  21. Thorner, A.R., Parker, J.S., Hoadley, K.A. & Perou, C.M. Potential tumor suppressor role for the c-Myb oncogene in luminal breast cancer. PLoS ONE 5, e13073 (2010).

    Article  Google Scholar 

  22. Troester, M.A. et al. Activation of host wound responses in breast cancer microenvironment. Clin. Cancer Res. 15, 7020–7028 (2009).

    Article  CAS  Google Scholar 

  23. Usary, J. et al. Mutation of GATA3 in human breast tumors. Oncogene 23, 7669–7678 (2004).

    Article  CAS  Google Scholar 

  24. Harrell, J.C. et al. Endothelial-like properties of claudin-low breast cancer cells promote tumor vascular permeability and metastasis. Clin. Exp. Metastasis 31, 33–45 (2014).

    Article  CAS  Google Scholar 

  25. Wong, D.J. et al. Module map of stem cell genes guides creation of epithelial cancer stem cells. Cell Stem Cell 2, 333–344 (2008).

    Article  CAS  Google Scholar 

  26. Ji, H. et al. LKB1 modulates lung cancer differentiation and metastasis. Nature 448, 807–810 (2007).

    Article  CAS  Google Scholar 

  27. Saal, L.H. et al. Poor prognosis in carcinoma is associated with a gene expression signature of aberrant PTEN tumor suppressor pathway activity. Proc. Natl. Acad. Sci. USA 104, 7564–7569 (2007).

    Article  CAS  Google Scholar 

  28. Glinsky, G.V., Berezovska, O. & Glinskii, A.B. Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer. J. Clin. Invest. 115, 1503–1521 (2005).

    Article  CAS  Google Scholar 

  29. Lim, E. et al. Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers. Nat. Med. 15, 907–913 (2009).

    Article  CAS  Google Scholar 

  30. van 't Veer, L.J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    Article  CAS  Google Scholar 

  31. Parker, J.S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).

    Article  Google Scholar 

  32. Chang, J.T. et al. SIGNATURE: a workbench for gene expression signature analysis. BMC Bioinformatics 12, 443 (2011).

    Article  Google Scholar 

  33. Leone, G. et al. Myc requires distinct E2F activities to induce S phase and apoptosis. Mol. Cell 8, 105–113 (2001).

    Article  CAS  Google Scholar 

  34. Grandis, J.R. et al. Requirement of Stat3 but not Stat1 activation for epidermal growth factor receptor–mediated cell growth in vitro. J. Clin. Invest. 102, 1385–1392 (1998).

    Article  CAS  Google Scholar 

  35. Weigman, V.J. et al. Basal-like breast cancer DNA copy number losses identify genes involved in genomic instability, response to therapy, and patient survival. Breast Cancer Res. Treat. 133, 865–880 (2012).

    Article  CAS  Google Scholar 

  36. Park, K., Kwak, K., Kim, J., Lim, S. & Han, S. c-Myc amplification is associated with HER2 amplification and closely linked with cell proliferation in tissue microarray of nonselected breast cancers. Hum. Pathol. 36, 634–639 (2005).

    Article  CAS  Google Scholar 

  37. Nevins, J.R. The Rb/E2F pathway and cancer. Hum. Mol. Genet. 10, 699–703 (2001).

    Article  CAS  Google Scholar 

  38. Wirapati, P. et al. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res. 10, R65 (2008).

    Article  Google Scholar 

  39. Perreard, L. et al. Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay. Breast Cancer Res. 8, R23 (2006).

    Article  Google Scholar 

  40. Hoadley, K.A. et al. Multi-platform integration of 12 cancer types reveals cell-of-origin classes with distinct molecular signatures. Cell 158, 1–16 (2014).

    Article  Google Scholar 

  41. Hoeflich, K.P. et al. In vivo antitumor activity of MEK and phosphatidylinositol 3-kinase inhibitors in basal-like breast cancer models. Clin. Cancer Res. 15, 4649–4664 (2009).

    Article  CAS  Google Scholar 

  42. Rody, A. et al. T-cell metagene predicts a favorable prognosis in estrogen receptor–negative and HER2-positive breast cancers. Breast Cancer Res. 11, R15 (2009).

    Article  Google Scholar 

  43. Kurogane, Y. et al. FGD5 mediates proangiogenic action of vascular endothelial growth factor in human vascular endothelial cells. Arterioscler. Thromb. Vasc. Biol. 32, 988–996 (2012).

    Article  CAS  Google Scholar 

  44. Kishi, N. et al. Murine homologs of deltex define a novel gene family involved in vertebrate Notch signaling and neurogenesis. Int. J. Dev. Neurosci. 19, 21–35 (2001).

    Article  CAS  Google Scholar 

  45. Matsuno, K., Diederich, R.J., Go, M.J., Blaumueller, C.M. & Artavanis-Tsakonas, S. Deltex acts as a positive regulator of Notch signaling through interactions with the Notch ankyrin repeats. Development 121, 2633–2644 (1995).

    CAS  PubMed  Google Scholar 

  46. Benelli, D., Cialfi, S., Pinzaglia, M., Talora, C. & Londei, P. The translation factor eIF6 is a Notch-dependent regulator of cell migration and invasion. PLoS ONE 7, e32047 (2012).

    Article  CAS  Google Scholar 

  47. Miluzio, A. et al. Impairment of cytoplasmic eIF6 activity restricts lymphomagenesis and tumor progression without affecting normal growth. Cancer Cell 19, 765–775 (2011).

    Article  CAS  Google Scholar 

  48. Lyng, H. et al. Gene expressions and copy numbers associated with metastatic phenotypes of uterine cervical cancer. BMC Genomics 7, 268 (2006).

    Article  Google Scholar 

  49. Tan, X.L. et al. Genetic variation predicting cisplatin cytotoxicity associated with overall survival in lung cancer patients receiving platinum-based chemotherapy. Clin. Cancer Res. 17, 5801–5811 (2011).

    Article  CAS  Google Scholar 

  50. Gandin, V. et al. Eukaryotic initiation factor 6 is rate-limiting in translation, growth and transformation. Nature 455, 684–688 (2008).

    Article  CAS  Google Scholar 

  51. Biffo, S. et al. Isolation of a novel β4 integrin–binding protein (p27(BBP)) highly expressed in epithelial cells. J. Biol. Chem. 272, 30314–30321 (1997).

    Article  CAS  Google Scholar 

  52. Shi, Z.Z. et al. Genomic alterations with impact on survival in esophageal squamous cell carcinoma identified by array comparative genomic hybridization. Genes Chromosom. Cancer 50, 518–526 (2011).

    Article  CAS  Google Scholar 

  53. Liu, L., Wang, Y.D., Wu, J., Cui, J. & Chen, T. Carnitine palmitoyltransferase 1A (CPT1A): a transcriptional target of PAX3-FKHR and mediates PAX3-FKHR–dependent motility in alveolar rhabdomyosarcoma cells. BMC Cancer 12, 154 (2012).

    Article  Google Scholar 

  54. Samudio, I. et al. Pharmacologic inhibition of fatty acid oxidation sensitizes human leukemia cells to apoptosis induction. J. Clin. Invest. 120, 142–156 (2010).

    Article  CAS  Google Scholar 

  55. Pacilli, A. et al. Carnitine-acyltransferase system inhibition, cancer cell death, and prevention of myc-induced lymphomagenesis. J. Natl. Cancer Inst. 105, 489–498 (2013).

    Article  CAS  Google Scholar 

  56. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank members of our laboratory for helpful discussion and suggestions. Research reported in this publication was supported by the National Cancer Institute of the US National Institutes of Health under award number K99-CA166228-01A1 to M.L.G. Additional funding for research reported in this study was provided by the National Cancer Institute of the US National Institutes of Health Breast SPORE program grant P50-CA58223-09A1 and RO1-CA148761-04, as well as grants from the Susan G. Komen for the Cure and the Breast Cancer Research Foundation to C.M.P.

Author information

Authors and Affiliations

  1. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Michael L Gatza, Grace O Silva, Joel S Parker, Cheng Fan & Charles M Perou

  2. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Michael L Gatza, Grace O Silva, Joel S Parker & Charles M Perou

  3. Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Grace O Silva & Charles M Perou

  4. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Charles M Perou

Authors
  1. Michael L Gatza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Grace O Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joel S Parker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cheng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Charles M Perou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.L.G., J.S.P. and C.M.P. conceived and designed the study. M.L.G., G.O.S. and C.F. performed analyses. M.L.G. and C.M.P. wrote the manuscript. All authors have reviewed and approved the final manuscript.

Corresponding author

Correspondence to Charles M Perou.

Ethics declarations

Competing interests

C.M.P. is an equity stock holder and board of director member of BioClassifier LLC and GeneCentric Diagnostics. C.M.P. is also listed as an inventor on a patent application for the PAM50 and VEGF-signature molecular assays. J.S.P. is also listed as an inventor on a patent application for the PAM50 and VEGF-signature molecular assays.

Integrated supplementary information

Supplementary Figure 1 Patterns of pathway activity correspond with molecular subtypes of breast cancer.

Analysis of molecular subtypes of breast cancer based on 52 gene expression signature scores. Euclidean distance was used to calculate the relationship between samples based on scores of 52 gene express signatures. Samples are commonly ordered on the X and Y axis according to molecular subtype. These results demonstrate high concordance within a subtype (dark blue), and lower concordance across subtypes; each sample versus itself is the blue diagonal line.

Supplementary Figure 2 Correlation between calculated pathway activity.

A Pearson correlation matrix of each signature versus all other signatures (including itself as the diagonal line) demonstrates a high degree of concordance amongst independently developed gene expression signatures measuring similar or associated pathways. Red indicates high positive correlation and blue a strong anti-correlation.

Supplementary Figure 3 Identification of pathway-specific copy number alterations by Spearman rank correlation.

A Spearman rank correlation, both positive (red) and negative (blue) were used to identify associations between predicted genomic signature pathway activity and gene-level DNA copy number content (n=476). The negative log10 Bonferroni adjust p-values are plotted according to chromosomal position. Chromosomal borders are delineated by vertical black lines.

Supplementary Figure 4 Identification of pathway-specific copy number alterations based on frequency of gains or losses calculated by Fisher’s exact test.

A Fisher’s exact test was used to calculate the statistical significance of the frequency of copy number gains (red) or losses (blue) in samples with the highest (top quartile) pathway signature activity relative to all other samples (n=476). The negative log10 Bonferroni adjust p-values are plotted according to chromosomal position. Vertical black lines indicate chromosomal borders.

Supplementary Figure 5 Patterns of pathway activity in human breast cancer cell lines.

The scored pathway activity for a panel of 51 breast cancer cell lines (GSE12777) was calculated for the 52 pathway signatures. Of these cell lines, 27 which are denoted by black squares in lower panel were subjected to a genome-wide RNAi screen.

Supplementary Figure 6 Identification of essential genes in proliferative breast cancer cell lines.

Identification of genes essential for cell viability in vitro in the context of the 11-gene PAM50 Proliferation signature in (A) all cell line samples and (B) in luminal and HER2+ breast cancer cell lines. The negative log10 Spearman rank correlation p values are plotted for each gene relative to chromosomal position.

Supplementary Figure 7 Correlation between candidate gene mRNA expression and DNA copy number status in TCGA samples.

The mRNA expression levels of the 21 identified candidate genes that are required for cell viability and are uniquely amplified in highly proliferative luminal breast tumors. In each plot, the mRNA levels from the TCGA data are compared in those tumors with amplifications versus all others. Of the 21 genes, two (ANKRD56 and TMEM189) were not present on the mRNA expression array and are not included here. Of the remaining 19 genes, 12 had a significant relationship (p<0.05) between copy number status and mRNA expression levels.

Supplementary Figure 8 Correlation between candidate gene mRNA expression and DNA copy number status in METABRIC samples.

The mRNA expression levels of the 12 identified candidate genes that are required for cell viability and are uniquely amplified in highly proliferative luminal breast tumors were analyzed within the context of copy number level in the METABRIC dataset. Of these 12 genes, three (SNX21, ZBTB46 and DNAJC5) were not present on both of the METABRIC data platforms (mRNA expression and copy number). Of the remaining 9 genes, all had a significant relationship (p<0.05) between copy number status and mRNA expression levels.

Supplementary Figure 9 Validation of increased candidate gene copy number status in highly proliferative luminal breast tumors in METABRIC samples.

The relationship between amplification of each candidate gene within the context of highly proliferative luminal breast tumors was examined in the METABRIC dataset. Of the nine candidate genes, eight showed a significant enrichment in highly proliferative (top quartile) luminal breast tumors.

Supplementary Figure 10 Candidate gene expression correlation with PAM50 proliferation score independent of copy number status.

The relationship between mRNA expression and the PAM50 proliferation signature was determined independent of copy number status (t-test) in the TCGA (n=388) and METABRIC (n=1,333) luminal/ ER+ subset of patients. Three classes of genes were identified (top rows) those that have a positive correlation with the signature score irrespective of CN status (EIF2S2, EIF6, MRPS23, CPT1A), those that have an inverse correlation (DTX3) and those that do not show a consistent pattern between datasets (FGD5, METTL6, SLC2A10).

Supplementary Figure 11 Amplification status of a subset of candidate genes has no reproducible effect on prognosis.

Kaplan-Meier survival analysis based upon the amplification status of highly proliferative luminal tumor genes. No consistent difference in disease specific survival was observed for EIF2S2 (A, D), EIF6 (B, E) or SLC2A10 (C, F) when comparing luminal tumors characterized by amplification of each candidate gene relative to luminal tumors without an amplification (log rank p>0.05) in the TCGA (A-C) and METABRIC (D-F) datasets.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11 (PDF 5962 kb)

Supplementary Tables 1–5

Supplementary Tables 1–5 (XLSX 508 kb)

Supplementary Tables 6–18

Supplementary Tables 6–18 (XLSX 16763 kb)

Supplementary Tables 19–31

Supplementary Tables 19–31 (XLSX 16313 kb)

Supplementary Tables 32–44

Supplementary Tables 32–44 (XLSX 17009 kb)

Supplementary Tables 45–57

Supplementary Tables 45–57 (XLSX 16723 kb)

Supplementary Tables 58–67

Supplementary Tables 58–67 (XLSX 9222 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gatza, M., Silva, G., Parker, J. et al. An integrated genomics approach identifies drivers of proliferation in luminal-subtype human breast cancer. Nat Genet 46, 1051–1059 (2014). https://doi.org/10.1038/ng.3073

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3073

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research