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Proteasome machinery is instrumental in a common gain-of-function program of the p53 missense mutants in cancer

Nature Cell Biology volume 18, pages 897–909 (2016)Cite this article

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Abstract

In cancer, the tumour suppressor gene TP53 undergoes frequent missense mutations that endow mutant p53 proteins with oncogenic properties. Until now, a universal mutant p53 gain-of-function program has not been defined. By means of multi-omics: proteome, DNA interactome (chromatin immunoprecipitation followed by sequencing) and transcriptome (RNA sequencing/microarray) analyses, we identified the proteasome machinery as a common target of p53 missense mutants. The mutant p53–proteasome axis globally affects protein homeostasis, inhibiting multiple tumour-suppressive pathways, including the anti-oncogenic KSRP–microRNA pathway. In cancer cells, p53 missense mutants cooperate with Nrf2 (NFE2L2) to activate proteasome gene transcription, resulting in resistance to the proteasome inhibitor carfilzomib. Combining the mutant p53-inactivating agent APR-246 (PRIMA-1MET) with the proteasome inhibitor carfilzomib is effective in overcoming chemoresistance in triple-negative breast cancer cells, creating a therapeutic opportunity for treatment of solid tumours and metastasis with mutant p53.

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Figure 1: The proteasome is the most affected and conserved pathway controlled by missense mutant p53 variants in TNBC cell lines.
Figure 2: The proteasome expression signature is associated with a poor patient prognosis and a mutant TP53 status in breast cancer.
Figure 3: The proteasome activity is elevated in the presence of the GOF p53 mutants in various cancer models.
Figure 4: Mutant p53 cooperates with Nrf2 in binding and activating promoters of the 26S proteasome subunit genes.
Figure 5: The GOF p53 mutants interact with Nrf2 and are functionally sensitive to NRF2 silencing.
Figure 6: Mutant p53 exerts gain of function through the proteasome-mediated inhibition of tumour suppressors.
Figure 7: Mutant p53 targeting with APR-246 eliminates resistance to carfilzomib in TNBC cells.
Figure 8: Model representation of mutant p53 regulation of the proteasome machinery and its therapeutic implication.

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Proteomics Identifications Database

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Acknowledgements

We thank A. Testa for reading and editing the manuscript, and A. Kuczynski, L. Collavin, F. Mantovani, A. Rustighi and colleagues from LNCIB for helpful comments and discussions. We acknowledge K. Wiman and U. Björklund from Aprea for sharing the APR-246 compound and discussing the administration protocol. We thank D. D. Zhang for the gift of the Nrf2 expression vector. This work was supported by the European Research Council (ERC), the Italian Health Ministry and the Italian Association for Cancer Research (AIRC) to B.A. and the Italian Association for Cancer Research (AIRC) Special Program Molecular Clinical Oncology ‘5 per mille’ (grant no. 10016) and the Italian Ministry of Health (RF-2011-02346976), to G.D.S.; Y.C. was supported by the AIRC/FIRC fellowship; K.G.-W. was supported by the Polish Academy of Sciences; D.W. was a recipient of the FEBS postdoctoral fellowship.

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Authors and Affiliations

  1. Laboratorio Nazionale CIB, Area Science Park Padriciano, Trieste 34149, Italy

    Dawid Walerych, Kamil Lisek, Silvano Piazza, Yari Ciani, Emiliano Dalla, Katarzyna Rajkowska, Katarzyna Gaweda-Walerych, Eleonora Ingallina & Giannino Del Sal

  2. Dipartimento di Scienze della Vita-Università degli Studi di Trieste, Trieste, 34127, Italy

    Kamil Lisek, Eleonora Ingallina & Giannino Del Sal

  3. Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova 35128, Italy

    Roberta Sommaggio & Antonio Rosato

  4. Laboratory of Neurogenetics, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw 02106, Poland

    Katarzyna Gaweda-Walerych

  5. Department of Experimental Oncology, European Institute of Oncology (IEO), Milan 20141, Italy

    Claudia Tonelli & Bruno Amati

  6. Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan 20139, Italy

    Marco J. Morelli & Bruno Amati

  7. Laboratory of Experimental Oncology and Pharmacogenomics, IRCCS ‘Salvatore Maugeri’ Foundation, Pavia 27100, Italy

    Angela Amato, Vincenzo Eterno & Alberto Zambelli

  8. Unit of Medical Oncology, Azienda Ospedaliera Papa Giovanni XXIII, Bergamo 24127, Italy

    Alberto Zambelli

  9. Istituto Oncologico Veneto IOV-IRCCS, Padova 35128, Italy

    Antonio Rosato

  10. Department of Proteomics and Signal Transduction, Biochemical Proteomics Group, Max Planck Institute of Biochemistry, Martinsried D82152, Germany

    Jacek R. Wiśniewski

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  1. Dawid Walerych
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  2. Kamil Lisek
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Contributions

D.W. designed and performed the majority of the experiments, analysed the data, prepared the figures and wrote the paper. K.L. designed and performed the low-scale experiments, and prepared the figures. K.R., K.G.-W. and E.I. performed the low-scale experiments. R.S. and A.R. designed and performed the mouse xenograft experiments. S.P. and Y.C. performed the RNA-sequencing and the microarray data analysis and the patient data set association studies. C.T. and B.A. performed the ChIP-sequencing. M.J.M. and E.D. performed the ChIP-seq data analysis and the DNA motif identification. A.A., V.E. and A.Z. provided the human tumour samples, subtype identification, and p53 immunohistochemistry. J.R.W. performed the proteomic analysis and the proteomic data handling. G.D.S. supervised the project, designed experiments and wrote the paper.

Corresponding author

Correspondence to Giannino Del Sal.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Additional data for Fig. 1.

(a) Hierarchical clustering of gene expression data for each of the TNBC cell lines shown in Fig. 1b. In each expression matrix, after Z-score standardization, the genes with low standard deviation were filtered (MDA-MB-231: Genes 16804 (sd filter = 0.05), MDA-MB-468: Genes 11428 (sd filter = 0.1), BT-549: Genes 17531 (sd filter = 0.1), SUM149PT: Genes 14125 (sd filter = 0.1), HCC1395: Genes 10432 (sd filter = 0.1)). Increased (red) or decreased (green) expression of the genes is shown for each sample. Bars below the graphs identify the samples subjected to Control (Ctrl) or TP53 (p53) silencing (n = 3 for each cell line and condition); (b) Principal component analysis (PCA) was performed on the five cell lines expression matrix. The first 3 principal components are plotted in pairs, the emerging sample groups confirmed that each cell line is a homogeneous cell population irrespective of being silenced for TP53 (triangles) or not (circles). The difference among the cell lines is larger than between the controls and the TP53 silencing; (c) MDA-MB-231 cells RNA-seq samples quality control performed using FastQC. Selected results are shown. (d) MDA-MB-231 cells ChIP-seq samples quality control performed using FastQC. Selected results are shown. (e) Extended result from Fig. 1d: transcript levels of all human 26S proteasome and immunoproteasome subunits determined in five TNBC cell lines on mutant TP53 expression silencing using alternative siRNA—3′UTR-targeting siRNA II (normalized control level shown as the dashed line, each result is a mean of two independent experiments). # marks a possible off-target effect of TP53 siRNA II towards PSMD10 transcript. For individual expression values of each gene in each cell line, see Supplementary Table 3. Statistics source data for 1e provided in Supplementary Table 10.

Supplementary Figure 2 Additional data for Fig. 2.

(a) The mutant p53-related proteasome-ubiquitination pathway gene expression is more significantly associated with poor prognosis in breast cancer patients than of the genes from other top pathways derived from the common mutant p53 signature. HR—hazard ratio; logrank P—logrank test P value for the curves comparison (n = 3,458); (b) The high expression of 37 proteasome genes (‘whole proteasome signature’) is associated with the high grade of breast cancer in patients (grades marked 1–3). P value is derived from Mann–Whitney U test (n = 1401). Box plot centre represents the median, box extremes indicate first and third quartile, whiskers extend to the extreme values included in the interval calculated as ±1.58 IQR/sqrt(n) where IQR (interquartile range) is calculated as the third quartile minus the first quartile.; (c) The best recursive feature selection analysis scoring proteasome gene subset (composed of 6 genes, see Methods and Supplementary Table 8) was evaluated for the prognostic correlation in the breast cancer dataset (survival graph as in (a); HR—hazard ratio; logrank P—logrank test P value for the curves comparison, n = 3,458).; (d) The high expression of 37 proteasome genes is associated with the mutant TP53 status in the indicated cancer types (diff—difference in mean signature expression in mutant versus WT p53 status samples; P value is derived from Mann–Whitney U test). Number of patients for each cancer type is indicated below each graph, based on selection in Supplementary Table 14. Box plot centre represents the median, box extremes indicate first and third quartile, whiskers extend to the extreme values included in the interval calculated as ±1.58 IQR/sqrt(n) where IQR (interquartile range) is calculated as the third quartile minus the first quartile.

Supplementary Figure 3 Additional data for Fig. 3.

(a) Levels of selected proteasome subunits are lowered on TP53 expression silencing in the 5 TNBC cell lines. Below—a bar graphs demonstrating protein levels of proteasome subunits measured by densitometry in 5 TNBC cell lines with mutant p53 (averages of two western-blots per each result are used; two alternative siRNAs—TP53 siRNA I and II; normalized control silencing level shown as the dashed line); (b) Overexpressed mutant p53 variants rescue proteasome genes transcription in the stably silenced endogenous mutant TP53 background of MDA-MB-231 cells. Lower panel: western blot demonstrating stable silencing of mutant TP53 and expression of the mutant p53 variants (representative of 2 repeats); (c) trypsin-like proteasome activity is decreased in mutant p53 TNBC cell lines versus WT p53 cell lines (MCF10A and MCF7) on silencing of mutant TP53 or PSMA2 or proteasome inhibitor treatment (24 h; Carfilzomib, Bortezomib).; (d) IHC staining of p53 (brown) in representative samples from Fig. 3b with indicated numbers corresponding to the Supplementary Table 15; Scale bars are 100 μM; (e) trypsin-like proteasome activity in MCF10A cell lines treated with 20 μM Nutlin for 24 h, stably transfected with vector encoding shRNA targeting TP53 and indicated mutant p53 cds shRNA-resistant HA-tagged variants (+p53 changed residue); (f) Transcript levels of proteasome subunits in MCF10A cell lines treated with 20 μM Nutlin for 24 h, stably transfected with vector encoding shRNA targeting TP53 and indicated mutant p53 cds shRNA-resistant HA-tagged variants (+p53 changed residue); (g) Basal chymotrypsin-like and trypsin-like proteasome activities are elevated in the TNBC cell lines (mutant p53), as compared to the MCF10A cell line (WT p53).; (h) Transcript levels of proteasome subunits are decreased in the indicated non-breast cancer cell lines on mutant TP53 expression silencing. Control level is marked with the dashed line. (b, c, e–h) Means of n = 3 biologically independent samples with s.d. are shown, ANOVA test with Bonferroni correction: *P < 0.05, **P < 0.01, ***P < 0.001; Unprocessed original scans of blots are shown in Supplementary Fig. 9. Statistics source data for (a–c, e–h) provided in Supplementary Table 10.

Supplementary Figure 4 Additional data for Fig. 4.

(a) Integrative Genomics Viewer (IGV) snapshots at the selected proteasome subunit gene loci with shown ChIP-sequencing enrichment readouts for the indicated samples in the MDA-MB-231 cells—DO-1 p53 ChIP (red), IgG ChIP (green), ChIP input (blue). (*) indicate significant peaks called in range −/+500 bp of proteasome gene TSSes (Supplementary Table 4), other peak regions were hand-picked in IGV. Regions highlighted in red were used to design mutant p53 binding-region region primers for ChIP validation shown in Fig. 4a (primers listed in the Supplementary Table 7); (b) Western blot related to Fig. 4c (left panel) and Fig. 4d (right panel) showing protein levels of the transcription factors whose expression has been silenced in the indicated samples (representative of 3 repeats); (c) Effects of transfection of alternative siRNA for NRF2 (NRF2 II) and siRNA for TP53 on proteasome activity (middle panel) and transcription (right panel) are comparable with siRNA NRF2 I treatment shown in Fig. 4c and 4d. Means of data from two independent experiments; (d) Chromatin immunoprecipitation enrichment obtained with the indicated antibodies at PSMA2 and PSMC1 mutant p53 binding regions in the WT p53 MCF7 cells— no enrichment increase is observed for DO-1 ChIP. Means of data from two independent experiments; (e) Transcript levels of all human 26S proteasome and immunoproteasome subunits determined in MDA-MB-231 cells on mutant NRF2 expression silencing (normalized control level shown as the dashed line, means of data from two independent experiments). For individual expression values of each gene see Supplementary Table 5. Unprocessed original scans of blots are shown in Supplementary Fig. 9. Statistics source data for (c, d) provided in Supplementary Table 10.

Supplementary Figure 5 Additional data for Fig. 5.

(a) Mutant p53 co-immunoprecipitates (co-IP) with Nrf2 in MDA-MB-231 and MDA-MB-468 cell lysates but not in MCF-7 cell lysate (anti-Nrf2 antibody). Representative of 2 repeats; (b) Co-immunoprecipitation (co-IP) of Nrf2 (anti-Nrf2 antibody) and overexpressed WT or mutant (R175H and R280K) p53 in p53-null H1299 cells (representative of 2 repeats); (c) GST tagged mutant p53 variants (E.coli overexpressed) interact via DNA binding domain with overexpressed full length Nrf2 in the p53-null H1299 cell lysates. (FL- full length protein; DBD—DNA binding domain; N-term—amino terminal domain; C-term—carboxy terminal domain of p53). Below a ponceau-red stained membrane is shown with transferred GST-fusion constructs (representative of 3 repeats) and a scheme of the N-terminally GST-tagged p53 constructs used for the experiment; (d) The increased expression of PSMA2 and PSMC1 proteasome genes is blunted by silencing of TP53 or NRF2 in the presence of the overexpressed mutant p53 variants (R175H and R280K) in p53-null H1299 cells. The effect is absent in the WT p53 overexpressing cells (means of two independent experiments). Below—a western blot showing p53 and Nrf2 levels in H1299 on indicated silencing (representative of 2 repeats); (e) Nrf2 and p53 are present in the nuclei of MDA-MB-231 cells with or without oxidative stress. Cells optionally treated with Nrf2-targeting siRNA and/or for 6h with 50 μM of oxidative stress-inducing sodium arsenite (NaAsO2). Representative of 3 repeats; (f) Mutant p53 co-immunoprecipitates with Nrf2 in the nuclear fraction of MDA-MB-231 (representative of 3 repeats); (g) Mutant p53 regulates transcription of Nrf2-dependent oxidative stress induced gene HO-1 in the opposite manner to the proteasome genes. Means of two independent experiments. (h) Nrf2 and p53 co-localize in the nuclei of MDA-MB-231 with or without oxidative stress. Cells optionally treated for 6h with 50 μM of oxidative stress-inducing sodium arsenite (NaAsO2). Representative of 3 repeats); (i) Nrf2 and p53 co-localize in the nuclei of MCF10A control cells (WT p53) and MCF10A cells with silenced endogenous WT TP53 (sh p53) plus overexpressed mutant p53 variants (+p53 R280K. +p53 R175H). Representative of 3 repeats. Unprocessed original scans of blots are shown in Supplementary Fig. 9. Statistics source data for 5d, g provided in Supplementary Table 10.

Supplementary Figure 6 Additional data for Fig. 6.

(a) Simultaneous silencing of mutant TP53 and essential proteasome subunit PSMA2 concomitantly decreases MDA-MB-231 cells viability and induces apoptosis markers. Bar graph represents cell viability 48 h post silencing of mutant TP53, PSMA2 or both. Means of n = 4 biologically independent samples are shown with s.d., ANOVA test with Bonferroni correction: ***P < 0.001. Lower panel: western blot showing the silencing effects on p53/PSMA2 and induction of apoptosis markers: PARP p85 fragment and cleaved Caspase 3 (representative of 3 repeats).; (b) Protein stability of proteasome target proteins p21, p27, KSRP is increased on silencing of TP53, PSMA2 or treatment with the proteasome inhibitor Carfilzomib (CFZ) in MDA-MB-231 cells (half-lives and western blots are representatives of 2 repeats are shown); (c) KSRP protein does not interact with mutant p53 in MDA-MB-231 cells (representative of 2 repeats); (d) Protein levels of mutant p53-proteasome axis targets, on their silencing as described in Fig. 6c–e (representative of 3 repeats); (e) Effects of alternative siRNAs used for KHSRP and mutant TP53 silencing on the levels of oncosuppressive microRNAs (bar graphs). Means of two independent experiments; (f) Silencing of KHSRP (KSRP), CDKN1A (p21), CDKN1B (p27) suppresses cell cycle arrest induced by mutant TP53 knockdown in MDA-MB-231 cells. Additional silencing of KHSRP (KSRP), CDKN1A (p21) or CDKN1B (p27) most efficiently restores the normal cell cycle profile (marked with ∗). Representative of 3 repeats; (g) Mutant TP53 or PSMA2 silencing induces KSRP protein level increase in the TNBC cell lines. Representatives of 2 repeats for each cell line; (h) Mutant TP53 or PSMA2 silencing induces levels of oncosuppressive microRNAs Let-7a and miR30c, while silencing of KHSRP reduces them in the indicated TNBC cell lines. Means of n = 3 biologically independent samples are shown with s.d., ANOVA test with Bonferroni correction: *P < 0.05,**P < 0.01, ***P < 0.001; Unprocessed original scans of blots are shown in Supplementary Fig. 9. Statistics source data for (a, e, h) provided in Supplementary Table 10.

Supplementary Figure 7 Additional data for Fig. 7.

(a) TP53 silencing or targeting with SAHA (Vorinostat) or PRIMA-1 sensitizes TNBC but not WT p53 cell lines to the proteasome inhibitor Carfilzomib (CFZ). Viability post 24 h treatment is shown. (b) Drug-mediated inhibition of proteasome and mutant p53 synergistically decreases the proteasome activity in MDA-MB-231 cells. (c) 24 h treatment of MDA-MB-231 cells with SAHA (2,5 μM), PRIMA-1 (1 μM) or APR-246 (1 μM) plus the proteasome inhibitor Carfilzomib (CFZ; 12.5 nM) induces tumour suppressive proteins KSRP, PUMA, p21 and NOXA (latter 3 are WT p53 targets) and the apoptosis marker PARP p85 increase. Representative of 2 repeats; (d) Simultaneous administration of PRIMA-1 and the proteasome inhibitor Carfilzomib (CFZ) inhibits the growth of primary xenograft tumours more effectively than a combination of SAHA and CFZ. Means of n = 4 animals with s.e.m. are shown, ANOVA test with Bonferroni correction: *P < 0.05,**P < 0.01, ***P < 0.001; (e) Concomitant treatment of MDA-MB-231 cells with Carfilzomib (CFZ) and APR-246 eliminates Carfilzomib resistant colonies while combining CFZ or APR-246 Cisplatin, Doxorubicin or Paclitaxel does not increase their toxicity. Means of two independent experiments; (f) Introduction of mutant p53 variants to MCF10A cells with stably silenced WT p53 increases their resistance to proteasome inhibitor Carfilzomib (CFZ) but sensitizes them to the CFZ + APR-246 combination (viability, 24 h treatment). Means of two independent experiments; (g) Mutant TP53, NRF2 silencing or APR-246 (PRIMA-1MET) treatment reduces the proteasome genes PSMA2 (left graph) and PSMC1 (right graph) transcript increase due to the bounce-back effect post treatment with Carfilzomib (CFZ) in TNBC cell lines. Means of of two independent experiments; (h) Primary MDA-MB-231-Luc (mutant p53, TNBC) subcutaneous xenograft growth in SCID mice is significantly reduced compared to the DMSO (caliper measurement, means of n = 8 independent animals with s.e.m. are shown, significance for the time-course is indicated—Friedman nonparametric matched pairs test with Dunn’s correction; ***P < 0.001). (a, b) Means of n = 3 biologically independent samples with s.d. are shown, ANOVA test with Bonferroni correction: *P < 0.05, **P < 0.01, ***P < 0.001; Unprocessed original scans of blots are shown in Supplementary Fig. 9. Statistics source data for 7a-b, d-g provided in Supplementary Table 10.

Supplementary Figure 8 Additional data for Fig. 7.

Treatment of SCID mice with MDA-MB-231 cells-derived xenografts tumours using the combination of APR-246 (PRIMA-1MET) and Carfilzomib (CFZ) strongly reduces metastasis to lymph nodes and lungs. Photos of the tissue slices of lymph nodes (lymph nodes homolatreal to xenogafts—indicated by arrows; bar size—2 mm) and lungs (bar size—200 μm) with MDA-MB-231 metastasis IHC staining (human cytokeratin, brown) from remaining mice in the experiment shown in Fig. 7g.

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Walerych, D., Lisek, K., Sommaggio, R. et al. Proteasome machinery is instrumental in a common gain-of-function program of the p53 missense mutants in cancer. Nat Cell Biol 18, 897–909 (2016). https://doi.org/10.1038/ncb3380

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