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.

Gene expression analysis identifies global gene dosage sensitivity in cancer

Nature Genetics volume 47, pages 115–125 (2015)Cite this article

Subjects

Abstract

Many cancer-associated somatic copy number alterations (SCNAs) are known. Currently, one of the challenges is to identify the molecular downstream effects of these variants. Although several SCNAs are known to change gene expression levels, it is not clear whether each individual SCNA affects gene expression. We reanalyzed 77,840 expression profiles and observed a limited set of 'transcriptional components' that describe well-known biology, explain the vast majority of variation in gene expression and enable us to predict the biological function of genes. On correcting expression profiles for these components, we observed that the residual expression levels (in 'functional genomic mRNA' profiling) correlated strongly with copy number. DNA copy number correlated positively with expression levels for 99% of all abundantly expressed human genes, indicating global gene dosage sensitivity. By applying this method to 16,172 patient-derived tumor samples, we replicated many loci with aberrant copy numbers and identified recurrently disrupted genes in genomically unstable cancers.

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

Get just this article for as long as you need it

$39.95

Learn more

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

Figure 1: Transcriptional components (TCs).
Figure 2: FEN1 is required for homologous recombination–mediated repair.
Figure 3: FGM expression profiles.
Figure 4: The vast majority of genes are dosage sensitive.
Figure 5: The FGM landscape in 16,172 unrelated patient-derived cancer samples.
Figure 6: Associations with genomic instability.

References

  1. Fehrmann, R.S.N. et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 7, e1002197 (2011).

    Article  CAS  Google Scholar 

  2. Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    Article  CAS  Google Scholar 

  3. Stranger, B.E. et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853 (2007).

    Article  CAS  Google Scholar 

  4. Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013).

    Article  CAS  Google Scholar 

  5. Wolfe, C.J., Kohane, I.S. & Butte, A.J. Systematic survey reveals general applicability of 'guilt-by-association' within gene coexpression networks. BMC Bioinformatics 6, 227 (2005).

    Article  Google Scholar 

  6. Moynahan, M.E. & Jasin, M. Mitotic homologous recombination maintains genomic stability and suppresses tumorigenesis. Nat. Rev. Mol. Cell Biol. 11, 196–207 (2010).

    Article  CAS  Google Scholar 

  7. Tomlinson, C.G., Atack, J.M., Chapados, B., Tainer, J.A. & Grasby, J.A. Substrate recognition and catalysis by flap endonucleases and related enzymes. Biochem. Soc. Trans. 38, 433–437 (2010).

    Article  CAS  Google Scholar 

  8. Zheng, L. et al. Fen1 mutations result in autoimmunity, chronic inflammation and cancers. Nat. Med. 13, 812–819 (2007).

    Article  CAS  Google Scholar 

  9. Kikuchi, K. et al. Fen-1 facilitates homologous recombination by removing divergent sequences at DNA break ends. Mol. Cell. Biol. 25, 6948–6955 (2005).

    Article  CAS  Google Scholar 

  10. Pierce, A.J., Johnson, R.D., Thompson, L.H. & Jasin, M. XRCC3 promotes homology-directed repair of DNA damage in mammalian cells. Genes Dev. 13, 2633–2638 (1999).

    Article  CAS  Google Scholar 

  11. Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).

    Article  CAS  Google Scholar 

  12. Bryant, H.E. et al. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature 434, 913–917 (2005).

    Article  CAS  Google Scholar 

  13. Cvejic, A. et al. SMIM1 underlies the Vel blood group and influences red blood cell traits. Nat. Genet. 45, 542–545 (2013).

    Article  CAS  Google Scholar 

  14. Rietveld, C.A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

    Article  CAS  Google Scholar 

  15. Hakem, R., de la Pompa, J.L. & Mak, T.W. Developmental studies of Brca1 and Brca2 knock-out mice. J. Mammary Gland Biol. Neoplasia 3, 431–445 (1998).

    Article  CAS  Google Scholar 

  16. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

  17. Shah, S.P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

    Article  CAS  Google Scholar 

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

  19. Stransky, N. et al. The mutational landscape of head and neck squamous cell carcinoma. Science 333, 1157–1160 (2011).

    Article  CAS  Google Scholar 

  20. Caldas, C. et al. Frequent somatic mutations and homozygous deletions of the p16 (MTS1) gene in pancreatic adenocarcinoma. Nat. Genet. 8, 27–32 (1994).

    Article  CAS  Google Scholar 

  21. Monzon, J. et al. CDKN2A mutations in multiple primary melanomas. N. Engl. J. Med. 338, 879–887 (1998).

    Article  CAS  Google Scholar 

  22. Williamson, M.P., Elder, P.A., Shaw, M.E., Devlin, J. & Knowles, M.A. p16 (CDKN2) is a major deletion target at 9p21 in bladder cancer. Hum. Mol. Genet. 4, 1569–1577 (1995).

    Article  CAS  Google Scholar 

  23. Santarius, T., Shipley, J., Brewer, D., Stratton, M.R. & Cooper, C.S. A census of amplified and overexpressed human cancer genes. Nat. Rev. Cancer 10, 59–64 (2010).

    Article  CAS  Google Scholar 

  24. Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

  25. Hovey, R.M. et al. Genetic alterations in primary bladder cancers and their metastases. Cancer Res. 58, 3555–3560 (1998).

    CAS  PubMed  Google Scholar 

  26. Slamon, D.J. et al. Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science 244, 707–712 (1989).

    Article  CAS  Google Scholar 

  27. Sworczak, K. et al. Gene copy numbers of erbB oncogenes in human pheochromocytoma. Oncol. Rep. 9, 1373–1378 (2002).

    CAS  PubMed  Google Scholar 

  28. Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998).

    Article  CAS  Google Scholar 

  29. Torres, E.M. et al. Identification of aneuploidy-tolerating mutations. Cell 143, 71–83 (2010).

    Article  CAS  Google Scholar 

  30. Oromendia, A.B., Dodgson, S.E. & Amon, A. Aneuploidy causes proteotoxic stress in yeast. Genes Dev. 26, 2696–2708 (2012).

    Article  CAS  Google Scholar 

  31. Fan, C. et al. Concordance among gene-expression-based predictors for breast cancer. N. Engl. J. Med. 355, 560–569 (2006).

    Article  CAS  Google Scholar 

  32. Barrett, T. et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 41, D991–D995 (2013).

    Article  CAS  Google Scholar 

  33. Crijns, A.P.G. et al. Survival-related profile, pathways, and transcription factors in ovarian cancer. PLoS Med. 6, e24 (2009).

    Article  Google Scholar 

  34. Heijink, D.M. et al. A bioinformatical and functional approach to identify novel strategies for chemoprevention of colorectal cancer. Oncogene 30, 2026–2036 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank J.L. Senior for editing the manuscript. This work was financially supported by grants from the Netherlands Organization for Scientific Research (NWO-VENI grant 916-10135 to L.F., NWO VIDI grant 916-76062 to M.A.T.M.v.V. and NWO VIDI grant 917-14374 to L.F.), a Horizon Breakthrough grant from the Netherlands Genomics Initiative (grant 92519031 to L.F.), a grant from the Van der Meer–Boerema Foundation to M.K. and grants from the Dutch Cancer Society (RUG 2011-5093 to M.A.T.M.v.V. and RUG 2013-5960 to R.S.N.F.). In addition, this study was financed in part by the SIA-raakPRO subsidy for project BioCOMP. The research leading to these results has received funding from the European Community's Health Seventh Framework Programme (FP7/2007–2013) under grant agreement 259867. This publication was made possible through the support of a grant from the John Templeton Foundation. Additional support for E.A.S. was provided by the Leiden Centre for Data Sciences.

Author information

Author notes

  1. Rudolf S N Fehrmann and Juha M Karjalainen: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    Rudolf S N Fehrmann, Małgorzata Krajewska, Elisabeth G E de Vries & Marcel A T M van Vugt

  2. Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    Rudolf S N Fehrmann, Juha M Karjalainen, Harm-Jan Westra, Gerard J te Meerman, Cisca Wijmenga & Lude Franke

  3. National Center for Advancing Translational Sciences, US National Institutes of Health, Rockville, Maryland, USA

    David Maloney & Anton Simeonov

  4. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Tune H Pers & Joel N Hirschhorn

  5. Division of Endocrinology, Children's Hospital Boston, Boston, Massachusetts, USA

    Tune H Pers & Joel N Hirschhorn

  6. Center for Basic and Translational Obesity Research, Children's Hospital Boston, Boston, Massachusetts, USA

    Tune H Pers & Joel N Hirschhorn

  7. Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark

    Tune H Pers

  8. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Joel N Hirschhorn

  9. Groningen Bioinformatics Centre, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, the Netherlands

    Ritsert C Jansen

  10. Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

    Erik A Schultes & Herman H H B M van Haagen

  11. BioSemantics Group, Leiden Institute of Advanced Computer Science, Leiden University, Leiden, the Netherlands

    Erik A Schultes

Authors
  1. Rudolf S N Fehrmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juha M Karjalainen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Małgorzata Krajewska
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Harm-Jan Westra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Maloney
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anton Simeonov
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tune H Pers
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joel N Hirschhorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ritsert C Jansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Erik A Schultes
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Herman H H B M van Haagen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Elisabeth G E de Vries
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Gerard J te Meerman
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Cisca Wijmenga
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Marcel A T M van Vugt
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Lude Franke
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.S.N.F. and L.F. conceived this study. M.K. performed the in vitro experiments. D.M. and A.S. synthesized chemical compounds. R.S.N.F., J.M.K., T.H.P., J.N.H., R.C.J., E.A.S., H.H.H.B.M.v.H., H.-J.W., G.J.t.M., M.A.T.M.v.V. and L.F. performed analyses. R.S.N.F., J.M.K., T.H.P., E.G.E.d.V., C.W., M.A.T.M.v.V. and L.F. wrote the manuscript.

Corresponding authors

Correspondence to Rudolf S N Fehrmann or Lude Franke.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Note. (PDF 35773 kb)

Supplementary Table 1

Platform descriptives. The first column contains the platform identifiers used by the manufacturer (Affymetrix). The second column contains the accession identifier for the Gene Expression Omnibus (GEO). The third column contains the number of probes on each platform. The fourth column describes the species for which the platform was designed. The fifth column contains the number of samples initially downloaded from GEO. The sixth column contains the number of remaining samples after quality control. (XLSX 29 kb)

Supplementary Table 2

Distribution of samples according to MeSH anatomy tree structure. Overview of a sample MeSH annotation according to the MeSH anatomy tree structure. (XLSX 15 kb)

Supplementary Table 3

Principal-component analysis summary for GPL96. PCA summary for GPL96 (Homo sapiens, HG-U133A). The second column contains the percentage of explained variance per transcriptional component (TC). The third column contains the cumulative explained variance. The fourth column contains the split-half correlation per TC. The fifth column contains the Cronbach's α per TC. (XLSX 84 kb)

Supplementary Table 4

Principal-component analysis summary for GPL570. PCA summary for GPL570 (Homo sapiens, HG-U133 2.0). The second column contains the percentage of explained variance per transcriptional component (TC). The third column contains the cumulative explained variance. The fourth column contains the split-half correlation per TC. The fifth column contains the Cronbach's α per TC. (XLSX 84 kb)

Supplementary Table 5

Principal-component analysis summary for GPL1261. PCA summary for GPL1261 (Mus musculus, MG-430 2.0). The second column contains the percentage of explained variance per transcriptional component (TC). The third column contains the cumulative explained variance. The fourth column contains the split-half correlation per TC. The fifth column contains the Cronbach's α per TC. (XLSX 84 kb)

Supplementary Table 6

Principal component analysis summary for GPL1355. PCA summary for GPL1355 (Rat norvegicus, RG-230 2.0). The second column contains the percentage of explained variance per transcriptional component (TC). The third column contains the cumulative explained variance. The fourth column contains the split-half correlation per TC. The fifth column contains the Cronbach's α per TC. (XLSX 84 kb)

Supplementary Table 7

Gene set enrichment summary for GPL96. This table contains the number of gene sets per transcriptional component and per gene set database that were significantly enriched at a permutation-based false discovery rate of 5% for GPL96 (Homo sapiens, HG-U133A). (XLSX 54 kb)

Supplementary Table 8

Gene set enrichment summary for GPL570. This table contains the number of gene sets per transcriptional component and per gene set database that were significantly enriched at a permutation-based false discovery rate of 5% for GPL570 (Homo sapiens, HG-U133 2.0). (XLSX 75 kb)

Supplementary Table 9

Gene set enrichment summary for GPL1261. This table contains the number of gene sets per transcriptional component and per gene set database that were significantly enriched at a permutation-based false discovery rate of 5% for GPL1261 (Mus musculus, MG-430 2.0). (XLSX 70 kb)

Supplementary Table 10

Gene set enrichment summary for GPL1355. This table contains the number of gene sets per transcriptional component and per gene set database that were significantly enriched at a permutation-based false discovery rate of 5% for GPL1355 (Rat norvegicus, RG-230 2.0). (XLSX 59 kb)

Supplementary Table 11

Estimated sensitivity and specificity. We estimated the sensitivity for the detection of copy number variations of different segment length (gain and loss). In addition, specificity was estimated for the different segment lengths. (XLSX 47 kb)

Supplementary Table 12

Distribution of tumor subtypes. We annotated each of the 16,172 cancer samples according to 1 of 41 cancer subtypes. The distribution of these 41 cancer subtypes is shown here. (XLSX 41 kb)

Supplementary Table 13

Final meta-analysis statistics for gene association with the degree of genomic instability. Genome-wide association analysis between individual genes (i.e., functional genomic mRNA expression signal) and the degree of genomic instability in 15,204 unrelated, patient-derived tumor samples was performed. Association was determined by the Pearson product-moment correlation coefficient within meta-analysis batches (272 meta-analysis batches with a range of 10–488 tumor samples per batch). Meta-analysis P values were calculated according to the Liptak's trend method (z-transformed P values, weighted according to the square root of the number of samples in a meta-analysis batch). (XLSX 1501 kb)

Supplementary Table 14

Predicted frequencies of increased signals. For each individual gene, the percentage of samples with a significantly increased signal was quantified across 41 tumor types. (TXT 6345 kb)

Supplementary Table 15

Predicted frequencies of decreased signals. For each individual gene, the percentage of samples with a significantly decreased signal was quantified across 41 tumor types. (TXT 6429 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fehrmann, R., Karjalainen, J., Krajewska, M. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat Genet 47, 115–125 (2015). https://doi.org/10.1038/ng.3173

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3173

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer