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Systematic analysis of alterations in the ubiquitin proteolysis system reveals its contribution to driver mutations in cancer


E3 ligases and degrons, the sequences they recognize in target proteins, are key parts of the ubiquitin-mediated proteolysis system. There are several examples of alterations of these two components of the system that have a role in cancer. Here we uncover the landscape of the contribution of such alterations to tumorigenesis across cancer types. We first systematically identified new instances of degrons across the human proteome by using a random forest classifier and validated the functionality of a dozen of them, exploiting somatic mutations across >7,000 tumors. We detected signals of positive selection across known and new degron instances. Our results reveal that several oncogenes are frequently targeted by mutations that affect the sequence of their degrons or their cognate E3 ubiquitin ligases, causing an abnormal increase in their protein abundance. Overall, an important number of driver mutations across primary tumors affect either degrons or E3-ubiquitin ligases.

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Fig. 1: Identification of new instances of known degrons.
Fig. 2: The effect of nonsynonymous mutations on the stability of proteins.
Fig. 3: Mutations affecting degrons increase the stability of proteins.
Fig. 4: Identification of new instances of de novo degrons.
Fig. 5: Degrons driving tumorigenesis.
Fig. 6: Downstream effects of driver E3 alterations.
Fig. 7: Contribution of the UPS to tumorigenesis.

Data availability

All data used in the analyses described in the paper are freely available within the public domain. The human tumor RNA-seq, somatic mutation and CNA data were derived from the TCGA Research Network. Specific links to each of the TCGA datasets are detailed in the Methods. Published TCGA RPPA data were downloaded from the TCPA portal (; version 4.2). MS datasets that were published previously and were reanalyzed here are available from the Clinical Proteomics Tumor Analysis Consortium. The CCLE datasets reanalyzed here can be obtained from the Broad Institute portal ( Specific links to each of the CCLE datasets are detailed in the Methods. The list of proteins involved in ubiquitination (UBSs) and deubiquitination (DUBs) was manually created by integrating previous knowledge from UniProt and E3NET (see above). Human protein–protein interaction data are available at STRING (9606.protein.links.detailed.v10.5.txt.gz; 19 February 2018). Amino acid sequences from 32,022 reviewed human protein isoforms are available from UniProt (see above). Degron motifs and degron instances in the human proteome are available at ELM (; 15 May 2019) and previous studies (see above). Phosphorylation and ubiquitination sites are available at PhosphositePlus (; 4 October 2018). The structures of an NFE2L2 fragment in complex with KEAP1 and the BTRC degron of CTNNB1 are available at PDB (see above). Pan-cancer gene fusions in the TCGA cohort are available at the Tumor Fusion Gene Data Portal (; 10 July 2018). The list of cancer-related genes is available at the Cancer Gene Census (download on 5 June 2019). The biomarkers of anticancer drug response are available from the Cancer Genome Interpreter (

Code availability

All software and data produced as part of the study (including scripts needed to reproduce all results described in the paper) are available at


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N.L.-B. acknowledges funding from the European Research Council (consolidator grant 682398) and the ERDF/Spanish Ministry of Science, Innovation and Universities–Spanish State Research Agency/DamReMap Project (RTI2018-094095-B-I00). A.G.-P. is supported by a Ramón y Cajal contract (RYC-2013-14554). IRB Barcelona is a recipient of a Severo Ochoa Centre of Excellence Award from the Spanish Ministry of Economy and Competitiveness (MINECO; Government of Spain) and is supported by CERCA (Generalitat de Catalunya). The results shown here are in whole or part based upon data generated by the TCGA Research Network. Data used in this publication were generated by the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC).

Author information




F.M.-J. prepared and carried out most analyses, including development of their statistical framework. F.M. carried out the estimation of excess mutations and contributed to the development of the statistical framework to compute protein expression residuals. E.L.-A. contributed to the annotation of degrons, curation of antibodies for RPPA data and preparation of Excel files. N.L.-B. and A.G.-P. conceived and oversaw the study. F.M.-J., N.L.-B. and A.G.-P. drafted the manuscript. All authors participated in interpretation and discussion of the results and in the final version of the manuscript.

Corresponding authors

Correspondence to Nuria Lopez-Bigas or Abel Gonzalez-Perez.

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Extended data

Extended Data Fig. 1 Identification of novel degron instances.

(a) Distribution of the values of biochemical properties of annotated degron instances and equally long randomly chosen sequences from the human proteome. The p-values were derived from two-tailed Mann-Whitney tests. Left N: number of validated degron instances; right N: number of random protein sequences sampled from the proteome. (b) Over or under representation of each amino acid (Fisher’s exact test odds ratio) across the sequence of annotated degron instances. Significant cases (p-value < 0.05) are circled in black. Relevant numbers are defined in (a). (c) Stratified 5-fold cross-validation ROC curve (as Fig. 1c) of a random forest classifier trained on annotated degrons and random sequences from the same set of proteins. Relevant numbers are defined in (a). (d) Precision/Recall of the random forest classifier described in the main paper (5-fold cross-validation). Relevant numbers are defined in (a). (e) Stratified 5-fold cross-validation ROC curve of a random forest classifier trained as described in the main paper, but adding random features highly correlated to the 11 used in the main paper. Relevant numbers are defined in (a). (f, g) Biochemical features at the top of the list of importance according to the classifiers trained in the main paper and above, for panel c. Bars represent the mean importance of each feature across the dataset, with the whiskers representing one standard deviation. (h) Stratified 5-fold cross-validation ROC curve resulting from the classification (with the random forest classifier described in the main paper) of experimentally identified FBXW11 degron instances and amino acid sequences of the same length randomly sampled from human proteins. Number of positive and negative instances defined at the top of the panel. (i) Stratified 5-fold cross-validation ROC curve resulting from the classification (with the random forest classifier described in the main paper) of experimentally identified FBXW11 degrons and random amino acid sequences from proteins deemed non FBXW11 targets. Number of positive and negative instances defined at the top of the panel. (j) Stratified 5-fold cross-validation ROC curve resulting from the classification (with the random forest classifier described in the main paper) of experimentally identified FBXW7 degrons and amino acid sequences of the same length randomly sampled from human proteins. Number of positive and negative instances defined at the top of the panel. (k-m) Correlation between the length of proteins and the number of matches (k), novel degron instances (l), novel degron instances with annotations (m) in their sequence. The numbers shown (R-value) correspond to the Pearson’s correlation coefficient. The trendline and its confidence intervals are shown as a line and a shaded area, respectively. N: number of proteins (k), novel degron instances (l), or novel degron instances with further supporting information (m).

Extended Data Fig. 2 Distribution of degron probability of the matches of each motif.

Each plot corresponds to the matches identified of one degron across the proteome. Degron probabilities are represented as a frequency histogram (solid light purple bars for motif matches and solid dark purple bars for annotated degron instances) and as the corresponding kernel-smoothed distribution (purple lines). Dashed vertical lines mark the site of the distribution that corresponds to the annotated degron with lowest probability, used as threshold to select high-confidence novel degron instances. In degron motifs with no annotated degron instance (that is, without solid dark purple histogram), the selected threshold is set at the lowest degron probability of any annotated degron (that is, 0.65). Values for all individual degrons are presented in Supplementary Table 2 and Supplementary Data.

Extended Data Fig. 3 Mutations affecting degrons increase the stability of proteins.

(a) Needle-plot representing the distribution of primary tumor mutations along the sequence of CTNNB1 (analogous to that of NFE2L2 in main Fig. 3a). (b) One recurrent mutation (S37C) projected onto the 3D structure of the CTNNB1-BTRC complex. (c, d) Comparisons of protein stability change upon mutations analogous to those represented in main Fig. 3e,f, restricted to tumors in which the gene harboring the degron under analysis is diploid. As in Fig. 3f, all p-values shown in this figure are derived from a one-tailed Mann-Whitney test. When two rows of p-values appear, the top value corresponds to the comparison between the distribution of stability change values of mutations in different groups and that of wild-type forms of the proteins, and the bottom value to the comparison with all missense mutations in the dataset. (e) Distribution of protein stability change caused by mutations in novel degrons instances in different quartiles of degron probability. (f, g) Comparisons of protein stability upon mutations analogous to those represented in main Fig. 3e,f, but carried out using cancer cell lines mutations. (h) Same as panel (e) for cancer cell lines mutations. (i) Thirteen proteins carrying mutations in novel degron instances exhibit a clear trend towards stability increase (determined using mass-spectrometry rather than RPPA as in previous examples), although non-significant due to lack of statistical power. (j) Distribution of stability change of proteins with non-synonymous mutations in different quartiles of VAF (that is, present in different fractions of tumor cells) which do not overlap with known or novel degron instances. The p-values correspond to the comparison (one-tailed Mann-Whitney test) between the distribution of stability change values of mutations in each quartile with respect to wild-type forms of the proteins. N: number of mutations in groups (in all panels). Boxplots in all panels are defined as in Fig. 2.

Extended Data Fig. 4 Identification of de novo degrons.

Identification of annotated degrons in CTNNB1 (a), NFE2L2 (b) and MET (c), PRKCA (d), BRAF (e), and ARAF (f) using the approach devised to identified de novo degrons. The panels follow the same composition and color codes as those in Fig. 4f,g. In parentheses, the names of the corresponding antibodies. N: number of tumor samples in each group.

Extended Data Fig. 5 Positive selection in degrons.

(a, b) QQ-plots relating the observed and expected distributions of p-values produced by the SMDeg (a) and FMDeg (b) tests on the TCGA pan-cancer cohort. N: number of tumor samples. (c, d) Novel degron instances that appear significant (FDR < 1%) in the SMDeg (c), or significant (FDR < 10%) or nearly significant (FDR < 25%) in the FMDeg test (d) across cancer cell lines. N: number of cancer cell lines. (e) De novo degron instances that appear significant (FDR < 1%) in the SMDeg test across TCGA primary tumors. N: number of tumor samples. (fh) Needle-plots representing the distribution of mutations in cancer cell lines along the sequences of ETV5 (f; significant in SMDeg), CCND3 (g; significant in SMDeg and FMDeg), USP36 (h; significant in SMDeg).

Extended Data Fig. 6 Driver E3s.

(a) Driver E3s across cancer cell lines are identified through signals of positive selection detected by OncodriveFML and dNdScv. Analogous to main Fig. 6a. N: number of cancer cell lines. (b) The combination of the two methods of positive selection employed yields 37 driver E3s across primary tumors. The size of the driver E3s correlates with their mutation frequency across TCGA samples. (c) Overlap between the lists of driver E3s identified in the study (red), annotated in the Cancer Gene Census (green) or identified in a recent analysis15 of TCGA datasets (blue).

Extended Data Fig. 7 TCGA tumors with actionable UPS alterations related to CCNE1.

(a) The bars represent the proportion of tumors in each cohort with CCNE1 alterations that could be targeted directly via CDK inhibitors (dark blue), or with alterations of FBXW7, with (medium blue) or without (light blue) increased stability of CCNE1 which could in principle be targeted indirectly. In parentheses, number of tumor samples in each cohort. (b) Mean percentage (and standard deviations as whiskers) of driver mutations in either driver E3s or driver degrons that do not occur in known cancer genes. In parentheses, number of tumor samples in each cohort.

Supplementary information

Supplementary Information

Supplementary Note

Reporting Summary

Supplementary Table

Supplementary Tables 1–6

Supplementary Data

Raw files containing proteome-wide annotated matches of degron motifs, degrons and E3s under positive selection. A README file contains a detailed description of the files enclosed within the zip file.

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Martínez-Jiménez, F., Muiños, F., López-Arribillaga, E. et al. Systematic analysis of alterations in the ubiquitin proteolysis system reveals its contribution to driver mutations in cancer. Nat Cancer 1, 122–135 (2020).

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