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Cited by 140Genomics, phytochemicals, and oxidative stressCancer Genomics and DiagnosticsChromatin Remodeling and Cancer

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A catalogue of somatic NRF2 gain-of-function mutations in cancer

Open access
Kerins, Michael John, Ooi, Aikseng·2018·Scientific Reports
DOI 10.1038/s41598-018-31281-0PMC PMC6110754
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Abstract

Identification and characterization of somatic mutations in cancer have important prognostication and treatment implications. Genes encoding the Nuclear factor (erythroid-derived 2)-like 2 (NRF2) transcription factor and its negative regulator, Kelch-like ECH-associated protein 1 (KEAP1), are frequently mutated in cancer. These mutations drive constitutive NRF2 activation and correlate with poor prognosis. Despite its apparent significance, a comprehensive catalogue of somatic NRF2 mutations across different tumor types is still lacking. Here, we catalogue NRF2 mutations in The Cancer Genome Atlas (TCGA) database. 226 unique NRF2-mutant tumors were identified from 10,364 cases. NRF2 mutations were found in 21 out of the 33 tumor types. A total of 11 hotspots were identified. Of these, mutation to the R34 position was most frequent. Notably, R34 and D29 mutations were overrepresented in bladder, lung, and uterine cancers. Analyses of corresponding RNA sequencing data using a de novo derived gene expression classifier showed that the R34 mutations drive constitutive NRF2 activation with a selection pressure biased against the formation of R34L. Of all R34 mutants, R34L conferred the least degree of protein stabilization, suggesting a pro-tumor NRF2 half-life threshold. Our findings offer a comprehensive catalogue of NRF2 mutations in cancer that can help prognostication and NRF2 research.

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Figure 1 Overview of TCGA dataset. (a) Number of cases analyzed within each tumor type. (b) Overlaps of unique mutations identified by different mutation calling algorithms. Mutations identified by two or more algorithms (shaded) were used in downstream analyses.
Figure 1 Overview of TCGA dataset. (a) Number of cases analyzed within each tumor type. (b) Overlaps of unique mutations identified by different mutation calling algorithms. Mutations identified by two or more algorithms (shaded) were used in downstream analyses.CC BY · PubMed Central
Figure 2 NRF2 and KEAP1 mutations are overrepresented in tumors associated with carcinogen exposure. (a) Number of cases identified with NRF2 mutation, KEAP1 mutation, or both. (b) Percentage of cases within tumor types harboring non-synonymous NRF2 mutations. (c) Percentage of cases within tumor types harboring non-synonymous KEAP1 mutations. (d) Median number of mutations per case by tumor type. Median number of mutations in NRF2-active cases was significantly higher than that in NRF2-inactive cases for the tumor types indicated (*indicates p < 0.05). (e) Median number of transversion mutations positively correlates with percentage of cases harboring NRF2 activation (p < 0.05), but median number of transition mutations does not (p > 0.05).
Figure 2 NRF2 and KEAP1 mutations are overrepresented in tumors associated with carcinogen exposure. (a) Number of cases identified with NRF2 mutation, KEAP1 mutation, or both. (b) Percentage of cases within tumor types harboring non-synonymous NRF2 mutations. (c) Percentage of cases within tumor types harboring non-synonymous KEAP1 mutations. (d) Median number of mutations per case by tumor type. Median number of mutations in NRF2-active cases was significantly higher than that in NRF2-inactive cases for the tumor types indicated (*indicates p < 0.05). (e) Median number of transversion mutations positively correlates with percentage of cases harboring NRF2 activation (p < 0.05), but median number of transition mutations does not (p > 0.05).CC BY · PubMed Central
Figure 3 NRF2 and KEAP1 are preferentially mutated at positions including and beyond NRF2-DLG and NRF2-ETGE motifs. (a) Number of nonsynonymous mutations at each NRF2 amino acid position from M1 to N605. Labeled amino acids were significantly enriched (p < 0.05). (b) Number of nonsynymous mutations at each NRF2 amino acid position along the DLG motif, shown from R25 to F37. R34 was the most frequently mutated NRF2 amino acid in the entire protein within this dataset. (c) Frequency of R34 mutant cases by tumor type. (d) R34 mutation is significantly enriched (p < 0.05) in specific tumor types. (e) Number of nonsynonymous mutations at each KEAP1 amino acid position from M1 to C624. Labeled amino acids were significantly enriched (p < 0.05).
Figure 3 NRF2 and KEAP1 are preferentially mutated at positions including and beyond NRF2-DLG and NRF2-ETGE motifs. (a) Number of nonsynonymous mutations at each NRF2 amino acid position from M1 to N605. Labeled amino acids were significantly enriched (p < 0.05). (b) Number of nonsynymous mutations at each NRF2 amino acid position along the DLG motif, shown from R25 to F37. R34 was the most frequently mutated NRF2 amino acid in the entire protein within this dataset. (c) Frequency of R34 mutant cases by tumor type. (d) R34 mutation is significantly enriched (p < 0.05) in specific tumor types. (e) Number of nonsynonymous mutations at each KEAP1 amino acid position from M1 to C624. Labeled amino acids were significantly enriched (p < 0.05).CC BY · PubMed Central
Figure 4 RNA sequencing reveals NRF2-activating mutation. (a) Schematic showing workflow for identifying an NRF2-activation gene signature. (b) Machine Learning Scores for sequential addition of genes identified 28 genes as the highest-scoring geneset. (c) Hierarchical clustering analysis of RNA sequencing data of all lung tumor (LUSC and LUAD) cases using Ward’s minimum variance method with the 28 gene signature. G1 was designated as the normal tissue cases, G2 as the NRF2-active group, and G3 as the NRF2-inactive group. Individual cases designated as normal, KEAP1-mutant, or NRF2 mutant are indicated with vertical lines below their clustered position. KEAP1 and NRF2 mutants were enriched in G2 relative to G3 (p < 0.05). R34 mutants were enriched in G2 relative to G3 (p < 0.05). (d) Heatmap showing coordinated increased expression of canonical NRF2 target genes in the G2 lung tumor cases.
Figure 4 RNA sequencing reveals NRF2-activating mutation. (a) Schematic showing workflow for identifying an NRF2-activation gene signature. (b) Machine Learning Scores for sequential addition of genes identified 28 genes as the highest-scoring geneset. (c) Hierarchical clustering analysis of RNA sequencing data of all lung tumor (LUSC and LUAD) cases using Ward’s minimum variance method with the 28 gene signature. G1 was designated as the normal tissue cases, G2 as the NRF2-active group, and G3 as the NRF2-inactive group. Individual cases designated as normal, KEAP1-mutant, or NRF2 mutant are indicated with vertical lines below their clustered position. KEAP1 and NRF2 mutants were enriched in G2 relative to G3 (p < 0.05). R34 mutants were enriched in G2 relative to G3 (p < 0.05). (d) Heatmap showing coordinated increased expression of canonical NRF2 target genes in the G2 lung tumor cases.CC BY · PubMed Central
Figure 5 R34 mutation is biased against R34L. (a) Genetic code table of possible mutations to R34 codon. (b) R34G was significantly enriched amongst the 34 R34 mutations identified, while R34L was absent in the TCGA dataset. (c) Distribution of the different variants of R34 mutations across tumor types. (d) Western blots of HEK293 cells co-transfected with MYC-tagged wildtype (WT) or mutant NRF2 and either KEAP1 (+) or empty vector control (−). KEAP1 overexpression cannot decrease NRF2-R34 mutants’ protein levels, except NRF2-R34L. ACTB was used as a loading control. MYC-NRF2 and KEAP1 were resolved on the same 7% SDS-PAGE gels for corresponding sets of samples, while ACTB was resolved on 15% gels. WT, E82G, and R34G were run on one set of gels, while R34L, R34P, and R34Q were run on a separate set of gels. Uncropped blots are available in Supplementary Fig. S5.
Figure 5 R34 mutation is biased against R34L. (a) Genetic code table of possible mutations to R34 codon. (b) R34G was significantly enriched amongst the 34 R34 mutations identified, while R34L was absent in the TCGA dataset. (c) Distribution of the different variants of R34 mutations across tumor types. (d) Western blots of HEK293 cells co-transfected with MYC-tagged wildtype (WT) or mutant NRF2 and either KEAP1 (+) or empty vector control (−). KEAP1 overexpression cannot decrease NRF2-R34 mutants’ protein levels, except NRF2-R34L. ACTB was used as a loading control. MYC-NRF2 and KEAP1 were resolved on the same 7% SDS-PAGE gels for corresponding sets of samples, while ACTB was resolved on 15% gels. WT, E82G, and R34G were run on one set of gels, while R34L, R34P, and R34Q were run on a separate set of gels. Uncropped blots are available in Supplementary Fig. S5.CC BY · PubMed Central
Figure 6 Mutation at R34 extends NRF2 half-life. (a) Representative western blot of MYC-tagged wildtype (WT) or mutant NRF2 protein levels decreasing over time following cycloheximide treatment (100 μg/mL). ACTB was used as a loading control. MYC-NRF2 was resolved on 7% SDS-PAGE gels, while ACTB was resolved on 15% gels. (b) NRF2 wildtype and mutant protein half-lives calculated from densitometry analyses of 3 biological replicates of cycloheximide chase. *Indicates p < 0.05 relative to WT by 2-tailed t-test and error bars represent ± SD. Uncropped blots are available in Supplementary Fig. S6.
Figure 6 Mutation at R34 extends NRF2 half-life. (a) Representative western blot of MYC-tagged wildtype (WT) or mutant NRF2 protein levels decreasing over time following cycloheximide treatment (100 μg/mL). ACTB was used as a loading control. MYC-NRF2 was resolved on 7% SDS-PAGE gels, while ACTB was resolved on 15% gels. (b) NRF2 wildtype and mutant protein half-lives calculated from densitometry analyses of 3 biological replicates of cycloheximide chase. *Indicates p < 0.05 relative to WT by 2-tailed t-test and error bars represent ± SD. Uncropped blots are available in Supplementary Fig. S6.CC BY · PubMed Central

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Metadata

Authors
Kerins, M. J., & Ooi, A.
Affiliations
University of Arizona (US)
Corresponding author
Ooi, Aikseng
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2018-08-27
Volume
8
Issue
1
Pages
12846-12846
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-018-31281-0
PMC
PMC6110754
OpenAlex
W2888569026
Cited by
140
References
67
Citations by year
2026: 14 · 2025: 13 · 2024: 18 · 2023: 18 · 2022: 19 · 2021: 22 · 2020: 26 · 2019: 10
FWCI
5.16
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UA Campus Repository (The University of Arizona) (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Science Foundation (DGE-1143953)
  • U.S. Department of Health &amp; Human Services | NIH | National Institute of Environmental Health Sciences (R21ES027920)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics, phytochemicals, and oxidative stress
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
Keywords
Nrf2, somatic mutations, gain-of-function mutations, Keap1, mutational hotspots, protein stabilization, gene expression classifier, bladder cancer, lung cancer, uterine cancer, cancer genomics, TCGA
MeSH terms
Kelch-Like ECH-Associated Protein 1, Gain of Function Mutation, Humans, Neoplasms, Signal Transduction, Sequence Analysis, DNA, Computational Biology, Amino Acid Substitution, Databases, Genetic, Cell Line, Tumor, NF-E2-Related Factor 2, Protein Stability

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