Mutational processes shape the landscape of TP53 mutations in human cancer


Unlike most tumor suppressor genes, the most common genetic alterations in tumor protein p53 (TP53) are missense mutations1,2. Mutant p53 protein is often abundantly expressed in cancers and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models3,4,5,6,7,8. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53 wild-type and null cell lines. We found that loss or dominant-negative inhibition of wild-type p53 function reliably enhanced cellular fitness. By integrating these data with the Catalog of Somatic Mutations in Cancer (COSMIC) mutational signatures database9,10, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.

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Fig. 1: Deletion of endogenous WT but not mutant TP53 impacts fitness in human cancer cells.
Fig. 2: Comprehensive mutational scanning of TP53.
Fig. 3: Tissue of origin-selective TP53 mutations are linked to specific mutational processes.
Fig. 4: The TP53 mutational spectrum modeled as a function of mutational signatures and phenotypic selection.

Data availability

All data sets analyzed in the current study are included in this published manuscript and its supplementary information files or can be found in the published works cited herein.


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This work was funded in part by grants from the US National Cancer Institute (U01 CA176058, U01 CA199253). T.P.H. is the recipient of training grants from the US National Institutes of Health (T32GM007753 and T32GM007226). G.G. was partially funded by the Paul C. Zamecnik Chair in Oncology from the Massachusetts General Hospital Cancer Center.

Author information




A.O.G. and W.C.H. designed the study. A.O.G., X.Y., and R.E.L. performed the experiments with help from T.P.H., D.Y.T., S.H.L., E.K., H.S.G., B.H., A.G., and B.F. A.O.G., X.Y., R.E.L., J.M.M., M.D., J.K., and D.E.R. analyzed the data with help from T.S., S.S., F.V., A.T., A.J.A., J.G.D., F.P., and G.G. A.O.G. and W.C.H. wrote the manuscript. D.E.R., C.M.J., M.M., and C.W.M.R. revised the manuscript.

Corresponding author

Correspondence to William C. Hahn.

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Competing interests

M.M. is a consultant for OrigiMed and receives research support from Bayer. W.C.H. is a consultant for KSQ Therapeutics.

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Integrated supplementary information

Supplementary Figure 1 Suppression of endogenous wild-type but not mutant TP53 impacts cellular fitness in human cancer cells.

a,b, Comparison of enrichment scores (Cell 170, 564–576, 2017) for RNAi reagents targeting TP53 (a) or MDM2 (b) in cell lines with differing p53 status. The functional and genetic p53 status of each cell line was defined using publicly available p53 target gene expression, nutlin-3 sensitivity, and TP53 sequencing data (Supplementary Table 1). Only cell lines with concordant functional and genetic classifications were included in the analyses in ac. Each point represents the gene-level score for a given cell line, and error bars indicate the mean and s.d. of each group. c, p53 protein expression in cancer cell lines as measured by reverse-phase protein array (RPPA). dg, Comparison of enrichment scores for TP53-targeting reagents for cell lines with concordant (d,e) or discordant (f,g) genetic and functional TP53 status (*P < 0.05, ****P < 0.0001, two-tailed Welch’s t-test).

Supplementary Figure 2 Response to MDM2 inhibition and DNA damage in cells expressing exogenous wild-type or mutant p53.

a, p53WT and p53NULL A549 cells were infected with a lentivirus encoding wild-type p53, mutant p53 p.Pro278Ala, or Renilla luciferase as a negative control and treated with DMSO vehicle or nutlin-3 at 10 µM for 24 h. be, Immunoblots were performed on whole-cell lysate using antibodies targeting p53, p21, or β-actin and detected using HRP-conjugated secondary antibodies. Cells were seeded into the wells of 96-well dishes at 200 cells/well, treated with the indicated compounds and subjected to CellTiter-Glo assays 7 d later. Luminescence readings were normalized to those for DMSO-treated wells (100% luminescence) and wells lacking cells (0% luminescence). b, Nutlin-3, 2.5 µM. c, Nutlin-3, 5 µM. d, Etoposide, 5 µM. e, Etoposide, 5 µM. Data from 3–5 independent experiments are presented as the mean ± s.e.m. (*P < 0.05, **P < 0.01, ***P < 0.001, two-tailed paired t-test).

Supplementary Figure 3 Differential fitness of isogenic p53WT and p53NULL cells treated with chemotherapeutic agents.

a, Graphical overview of the LucifeRace competition assay. b, p53WT and p53NULL cells were infected at low MOI with lentivirus expressing either firefly (FF) or Renilla luciferase. Paired cell lines were mixed together at a 1:1 ratio (p53WT-Renilla with p53NULL-FF, and p53WT-FF with p53NULL-Renilla) and seeded into two replica plates. Twenty-four hours later, cells were treated with the indicated drugs at six doses and incubated for 2 d more. One of the plates was then subjected to a dual-luciferase assay and the other was used for continued passaging. Raw FF and Renilla luminescence values for each well were normalized to their respective DMSO vehicle-treated wells and are presented as a ratio. Drugs were delivered as a twofold-dilution series to achieve the following high doses: gemcitabine (100 nM), 5-fluorouracil (10 µM), actinomycin D (10 nM), oxaliplatin (25 µM), bleomycin (5 µM), doxorubicin (50 nM), etoposide (2.5 µM), nutlin-3 (20 µM), vinblastine (5 nM), and paclitaxel (10 nM). c,d, LucifeRace WT:KO luminescence ratios plotted over time for each dose of etoposide and doxorubicin. ej, CellTiter-Glo assays were performed on cells treated with etoposide or doxorubicin and dose–response curves were fit using GraphPad Prism 7 software. Representative curves for each drug are shown (e,f), along with the best fit values for the curve midpoint (EC50; g,h) and minimum (Curve Bottom; i–,j) (mean ± s.e.m., three independent experiments, two-tailed paired t-test, **P< 0.01).

Supplementary Figure 4 Comparison of TP53 MITE screen data with existing datasets.

ai, log2-normalized read counts (a,d,e) and Z-scores (b,e,h) were plotted for each experimental condition and replicate. The Pearson correlation (R2) was calculated for each plot using GraphPad Prism 7 software. c,f,i, Average Z-scores plotted at each codon position. j, Heat map of position-level average Z-scores for each screen condition plotted in line with position-level average transcriptional activity in yeast at the p21 promoter (Proc. Natl. Acad. Sci. USA 100, 8424–8429, 2003), evolutionary conservation scores derived using Align-GVGD (Nucleic Acids Res. 34, 1317–1325, 2006), and total number of mutations found at each codon in the IARC somatic TP53 mutation database (Human Mutation 37, 865–876, 2016; Human Mutation 28, 622–629, 2007). k, Pearson correlation matrices comparing the indicated datasets. Nut, nutlin-3; Eto, etoposide; EvoCon, evolutionary conservation. l, Density plots of combined phenotype scores for the indicated allele classes (left) with common somatic TP53 mutations and common TP53 germline polymorphisms highlighted along the distribution (right). m, Transcriptional activity of common somatic mutations and common germline polymorphisms measured in yeast (mean of eight activity reporters + s.e.m.) (Proc. Natl. Acad. Sci. USA 100, 8424–8429, 2003).

Supplementary Figure 5 Identification of TP53 alleles that exhibit assay-specific functions.

ac, Alleles were ranked according to the mean effect size from the two independent experiments with error bars representing the range of Z-scores. df, xy scatterplots of average Z-scores for the three pairs of screen conditions. g,h, Dominant-negative and loss-of-function alleles (defined by Z-scores that were 3 s.d. away from the mean Z-score of all silent mutations) showed a high degree of overlap, especially missense mutations in the DNA-binding domain. TAD, transactivation domain; PRD, proline-rich domain; DBD, DNA-binding domain; 4D, tetramerization domain; CTD, C-terminal domain.

Supplementary Figure 6 Mutational signatures and phenotypic selection dictate the spectrum of recurrent TP53 alterations.

Generalized linear models were used to estimate the contributions made by mutational processes and phenotypic selection to the TP53 mutation counts in the IARC somatic R18 database (Human Mutation 37, 865–876, 2016; Human Mutation 28, 622–629, 2007). ac, Model predictions for each codon (a,b) or allele (c) are shown plotted against the observed mutation counts in the database. dk, Tenfold cross-validation (hk) yielded similar descriptive and predictive accuracies, standardized coefficients, and P values as the final combined model (dg), indicating that the improved performance of the combined model was not due to overfitting to the training data. lo, Examples of alleles characterized by high intrinsic mutability and strong phenotypic effects (Arg273His, Arg175His, and Arg248Trp), low intrinsic mutability but strong phenotypic effects (Thr125Pro, Cys124Gly, and Phe212Val), and high intrinsic mutability but weak phenotypic effects (Val157Ile, Arg290His, and Arg282Gln).

Supplementary Figure 7 Cancer cell lines harboring TP53 variants of uncertain significance display wild-type-like responses to p53 pathway perturbation.

a, p53 pathway scores were assigned to each cell line in the Project Achilles RNAi dataset using DEMETER scores for TP53, USP28, TP53BP1, CHEK2, ATM, CDKN1A, MDM2, MDM4, PPM1D, USP7, and UBE2D3. Density plots for cell lines with concordant genetic and functional p53 status are shown (left), and REH, RMUGS, and HCC2218, which bear the indicated TP53 mutations, are highlighted along the distribution (right). b, Combined phenotype scores were assigned to each TP53 allele using the data from all three MITE library screens. Density plots for the indicated allele classes are shown (left), and Arg181Cys, Arg283Cys, and Ala347Val alleles are highlighted along the distribution (right).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Note

Reporting Summary

Supplementary Data

Source data for Supplementary Figure 2

Supplementary Table 1

Cancer cell line TP53 classification matrix

Supplementary Table 2

Read counts for all TP53 codon variants

Supplementary Table 3

Z-scores, mutation probabilities and model predictions for all TP53 amino acid variants

Supplementary Table 4

Sequences of sgRNAs used to target TP53

Supplementary Table 5

Signature 4* mutation probabilities

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Giacomelli, A.O., Yang, X., Lintner, R.E. et al. Mutational processes shape the landscape of TP53 mutations in human cancer. Nat Genet 50, 1381–1387 (2018).

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