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Gene expression analysis identifies global gene dosage sensitivity in cancer

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.

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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.

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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

Authors and Affiliations

Authors

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.

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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)

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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

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