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Integrative multiomic approaches reveal ZMAT3 and p21 as conserved hubs in the p53 tumor suppression network

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Abstract

TP53, the most frequently mutated gene in human cancer, encodes a transcriptional activator that induces myriad downstream target genes. Despite the importance of p53 in tumor suppression, the specific p53 target genes important for tumor suppression remain unclear. Recent studies have identified the p53-inducible gene Zmat3 as a critical effector of tumor suppression, but many questions remain regarding its p53-dependence, activity across contexts, and mechanism of tumor suppression alone and in cooperation with other p53-inducible genes. To address these questions, we used Tuba-seqUltra somatic genome editing and tumor barcoding in a mouse lung adenocarcinoma model, combinatorial in vivo CRISPR/Cas9 screens, meta-analyses of gene expression and Cancer Dependency Map data, and integrative RNA-sequencing and shotgun proteomic analyses. We established Zmat3 as a core component of p53-mediated tumor suppression and identified Cdkn1a as the most potent cooperating p53-induced gene in tumor suppression. We discovered that ZMAT3/CDKN1A serve as near-universal effectors of p53-mediated tumor suppression that regulate cell division, migration, and extracellular matrix organization. Accordingly, combined Zmat3-Cdkn1a inactivation dramatically enhanced cell proliferation and migration compared to controls, akin to p53 inactivation. Together, our findings place ZMAT3 and CDKN1A as hubs of a p53-induced gene program that opposes tumorigenesis across various cellular and genetic contexts.

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Fig. 1: Genetic epistasis mapping reveals the effects of the p53-Zmat3 LUAD suppression axis in vivo.
Fig. 2: An in vivo CRISPR/Cas9 screen uncovers effectors of p53-mediated tumor suppression.
Fig. 3: Meta-analysis of p53-dependent expression data from mouse and human cells.
Fig. 4: ZMAT3 and CDKN1A are essential and evolutionarily conserved tumor suppressors in the p53 target gene network.
Fig. 5: Molecular analysis of genetic cooperation in the p53 tumor suppression pathway.
Fig. 6: ZMAT3 and p21 deficiency cooperatively enhance cell migration in 3D, similar to p53 loss.
Fig. 7: ZMAT3 and p21 are key effectors of p53-mediated tumor suppression.

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Data availability

The RNA sequencing data are available from the GEO under the accession number GSE289471 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE289471). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [61] partner repository with the dataset identifier PXD047240 and 10.6019/PXD047240. For DepMap analyses, somatic mutations, copy number status, CRISPR gene effect scores, gene expression, and cell line metadata files were downloaded from the DepMap Public 23Q4 Primary Files release. Previously published data on p53-dependent gene regulation were retrieved from www.TargetGeneReg.org [31], which contains 15 mouse and 57 human transcription profiling datasets. Additionally, genome-wide data on p53 binding were available from 9 curated ChIP datasets derived from mouse cells [33] and 28 ChIP datasets from human cells [62]. All other data supporting the findings of this study are in the article, supplemental information, or are available from the corresponding author upon reasonable request. Source data are provided in this paper.

Code availability

All code required to download, process, and visualize DepMap analyses is provided at https://github.com/brooksbenard/tp53_p21_zmat3/tree/main.

References

  1. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502:333.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Olivier M, Eeles R, Hollstein M, Khan MA, Harris CC, Hainaut P. The IARC TP53 database: new online mutation analysis and recommendations to users. Hum Mutat. 2002;19:607–14.

    Article  CAS  PubMed  Google Scholar 

  3. Brady CA, Jiang D, Mello SS, Johnson TM, Jarvis LA, Kozak MM, et al. Distinct p53 transcriptional programs dictate acute DNA damage responses and tumor suppression. Cell. 2011;145:571–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jiang D, Brady CA, Johnson TM, Lee EY, Park EJ, Scott MP, et al. Full p53 transcriptional activation potential is dispensable for tumor suppression in diverse lineages. Proc Natl Acad Sci USA. 2011;108:17123–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mello SS, Valente LJ, Raj N, Seoane JA, Flowers BM, McClendon J, et al. A p53 super-tumor suppressor reveals a tumor suppressive p53-Ptpn14-yap axis in pancreatic cancer. Cancer Cell. 2017;32:460–473.e6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ho T, Tan BX, Lane D. How the other half lives: what p53 does when it is not being a transcription factor. Int J Mol Sci. 2020;21:13.

    Article  CAS  Google Scholar 

  7. Boutelle AM, Attardi LD. p53 and tumor suppression: it takes a network. Trends Cell Biol. 2021;31:298–310.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Indeglia A, Murphy ME. Elucidating the chain of command: our current understanding of critical target genes for p53-mediated tumor suppression. Crit Rev Biochem Mol Biol. 2024;59:128–38.

    Article  CAS  PubMed  Google Scholar 

  9. Wang M, Attardi LD. A balancing act: p53 activity from tumor suppression to pathology and therapeutic implications. Annu Rev Pathol Mech Dis. 2022;17:205–26.

    Article  CAS  Google Scholar 

  10. Hassin O, Oren M. Drugging p53 in cancer: one protein, many targets. Nat Rev Drug Discov. 2023;22:127–44.

    Article  CAS  PubMed  Google Scholar 

  11. Janic A, Valente LJ, Wakefield MJ, Di Stefano L, Milla L, Wilcox S, et al. DNA repair processes are critical mediators of p53-dependent tumor suppression. Nat Med. 2018;24:947–53.

    Article  CAS  PubMed  Google Scholar 

  12. Moon SH, Huang CH, Houlihan SL, Regunath K, Freed-Pastor WA, Morris JP, et al. p53 represses the mevalonate pathway to mediate tumor suppression. Cell. 2019;176:564–580.e19.

    Article  CAS  PubMed  Google Scholar 

  13. Suzuki S, Venkatesh D, Kanda H, Nakayama A, Hosokawa H, Lee E, et al. GLS2 is a tumor suppressor and a regulator of ferroptosis in hepatocellular carcinoma. Cancer Res. 2022;82:3209–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Indeglia A, Leung JC, Miller SA, Leu JIJ, Dougherty JF, Clarke NL, et al. An African-specific variant of TP53 reveals PADI4 as a regulator of p53-mediated tumor suppression. Cancer Discov. 2023;13:1696–719.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bieging-Rolett KT, Kaiser AM, Morgens DW, Boutelle AM, Seoane JA, Van Nostrand EL, et al. Zmat3 is a key splicing regulator in the p53 tumor suppression program. Mol Cell. 2020;80:452–469.e9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Varmeh-Ziaie S, Okan I, Wang Y, Magnusson KP, Warthoe P, Strauss M, et al. Wig-1, a new p53-induced gene encoding a zinc finger protein. Oncogene. 1997;15:2699–704.

    Article  CAS  PubMed  Google Scholar 

  17. Israeli D, Tessler E, Haupt Y, Elkeles A, Wilder S, Amson R, et al. A novel p53-inducible gene, PAG608, encodes a nuclear zinc finger protein whose overexpression promotes apoptosis. EMBO J. 1997;16:4384–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Muys BR, Anastasakis DG, Claypool D, Pongor L, Li XL, Grammatikakis I, et al. The p53-induced RNA-binding protein ZMAT3 is a splicing regulator that inhibits the splicing of oncogenic CD44 variants in colorectal carcinoma. Genes Dev. 2020;35:102–16.

  19. Rogers ZN, McFarland CD, Winters IP, Naranjo S, Chuang CH, Petrov D, et al. A quantitative and multiplexed approach to uncover the fitness landscape of tumor suppression in vivo. Nat Methods. 2017;14:737.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cai H, Chew SK, Li C, Tsai MK, Andrejka L, Murray CW, et al. A functional taxonomy of tumor suppression in oncogenic KRAS-driven lung cancer. Cancer Discov. 2021;11:1754–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tang YJ, Xu H, Hughes NW, Kim SH, Ruiz P, Shuldiner EG, et al. Functional mapping of epigenetic regulators uncovers coordinated tumor suppression by the HBO1 and MLL1 complexes [Internet]. bioRxiv; 2024 [cited 22 Aug 2024]. p. 2024.08.19.607671. Available from: https://www.biorxiv.org/content/10.1101/2024.08.19.607671v1.

  22. Rogers ZN, McFarland CD, Winters IP, Seoane JA, Brady JJ, Yoon S, et al. Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice. Nat Genet. 2018;50:483–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kaiser AM, Gatto A, Hanson KJ, Zhao RL, Raj N, Ozawa MG, et al. p53 governs an AT1 differentiation programme in lung cancer suppression. Nature. 2023;619:851–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Blair LM, Juan JM, Sebastian L, Tran VB, Nie W, Wall GD, et al. Oncogenic context shapes the fitness landscape of tumor suppression. Nat Commun. 2023;14:6422.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lowe SW, Jacks T, Housman DE, Ruley HE. Abrogation of oncogene-associated apoptosis allows transformation of p53-deficient cells. Proc Natl Acad Sci USA. 1994;91:2026–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Attardi LD, Reczek EE, Cosmas C, Demicco EG, McCurrach ME, Lowe SW, et al. PERP, an apoptosis-associated target of p53, is a novel member of the PMP-22/gas3 family. Genes Dev. 2000;14:704–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Valente LJ, Tarangelo A, Li AM, Naciri M, Raj N, Boutelle AM, et al. p53 deficiency triggers dysregulation of diverse cellular processes in physiological oxygen. J Cell Biol. 2020 [cited 15 Sep 2020];219. Available from: https://rupress.org/jcb/article/219/11/e201908212/152074/p53-deficiency-triggers-dysregulation-of-diverse.

  28. Morgens DW, Deans RM, Li A, Bassik MC. Systematic comparison of CRISPR-Cas9 and RNAi screens for essential genes. Nat Biotechnol. 2016;34:634–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Drainas AP, Lambuta RA, Ivanova I, Serçin Ö, Sarropoulos I, Smith ML, et al. Genome-wide screens implicate loss of cullin ring ligase 3 in persistent proliferation and genome instability in TP53-deficient cells. Cell Rep. 2020;31:107465.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. el-Deiry WS, Tokino T, Velculescu VE, Levy DB, Parsons R, Trent JM, et al. WAF1, a potential mediator of p53 tumor suppression. Cell. 1993;75:817–25.

    Article  CAS  PubMed  Google Scholar 

  31. Fischer M, Schwarz R, Riege K, DeCaprio JA, Hoffmann S. TargetGeneReg 2.0: a comprehensive web-atlas for p53, p63, and cell cycle-dependent gene regulation. NAR Cancer. 2022;4:zcac009.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Fischer M. Census and evaluation of p53 target genes. Oncogene. 2017;36:3943.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans. Oncogene. 2019;38:4095–109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sinha S, Ayushman M, Tong X, Yang F. Dynamically crosslinked poly(ethylene-glycol) hydrogels reveal a critical role of viscoelasticity in modulating glioblastoma fates and drug responses in 3D. Adv Healthc Mater. 2023;12:2202147.

    Article  CAS  Google Scholar 

  35. Andrysik Z, Galbraith MD, Guarnieri AL, Zaccara S, Sullivan KD, Pandey A, et al. Identification of a core TP53 transcriptional program with highly distributed tumor suppressive activity. Genome Res. 2017;27:1645–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Brennan MS, Brinkmann K, Romero Sola G, Healey G, Gibson L, Gangoda L, et al. Combined absence of TRP53 target genes ZMAT3, PUMA and p21 cause a high incidence of cancer in mice. Cell Death Differ. 2023;31:159–69.

  37. Valente LJ, Gray DHD, Michalak EM, Pinon-Hofbauer J, Egle A, Scott CL, et al. p53 efficiently suppresses tumor development in the complete absence of its cell-cycle inhibitory and proapoptotic effectors p21, Puma, and Noxa. Cell Rep. 2013;3:1339–45.

    Article  CAS  PubMed  Google Scholar 

  38. Moyer SM, Wasylishen AR, Qi Y, Fowlkes N, Su X, Lozano G. p53 drives a transcriptional program that elicits a non-cell-autonomous response and alters cell state in vivo. Proc Natl Acad Sci USA. 2020;117:23663–73.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Narita M, Nuñez S, Heard E, Narita M, Lin AW, Hearn SA, et al. Rb-mediated heterochromatin formation and silencing of E2F target genes during cellular senescence. Cell. 2003;113:703–16.

    Article  CAS  PubMed  Google Scholar 

  40. Kasteri J, Das D, Zhong X, Persaud L, Francis A, Muharam H, et al. Translation control by p53. Cancers. 2018;10:133.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Tiu GC, Kerr CH, Forester CM, Krishnarao PS, Rosenblatt HD, Raj N, et al. A p53-dependent translational program directs tissue-selective phenotypes in a model of ribosomopathies. Dev Cell. 2021;56:2089–2102.e11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Akeno N, Miller AL, Ma X, Wikenheiser-Brokamp KA. p53 suppresses carcinoma progression by inhibiting mTOR pathway activation. Oncogene. 2015;34:589–99.

    Article  CAS  PubMed  Google Scholar 

  43. Kon N, Ou Y, Wang SJ, Li H, Rustgi AK, Gu W. mTOR inhibition acts as an unexpected checkpoint in p53-mediated tumor suppression. Genes Dev. 2021;35:59–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Coronel L, Häckes D, Schwab K, Riege K, Hoffmann S, Fischer M. p53-mediated AKT and mTOR inhibition requires RFX7 and DDIT4 and depends on nutrient abundance. Oncogene. 2022;41:1063–9.

    Article  CAS  PubMed  Google Scholar 

  45. Budanov AV, Karin M. p53 target genes sestrin1 and sestrin2 connect genotoxic stress and mTOR signaling. Cell. 2008;134:451–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen S, Thorne RF, Zhang XD, Wu M, Liu L. Non-coding RNAs, guardians of the p53 galaxy. Semin Cancer Biol. 2021;75:72–83.

    Article  CAS  PubMed  Google Scholar 

  47. McIlwain DR, Pan Q, Reilly PT, Elia AJ, McCracken S, Wakeham AC, et al. Smg1 is required for embryogenesis and regulates diverse genes via alternative splicing coupled to nonsense-mediated mRNA decay. Proc Natl Acad Sci. 2010;107:12186–91.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Desgrosellier JS, Cheresh DA. Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer. 2010;10:9–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. DiPersio CM, Longmate WM. Roles for integrin α3β1 in development and disease. In: Gullberg D, Eble JA, editors. Integrins in health and disease: key effectors of cell-matrix and cell-cell interactions [Internet]. Cham: Springer International Publishing; 2023 [cited 28 Aug 2024]. p. 27–95. Available from: https://doi.org/10.1007/978-3-031-23781-2_2.

  50. Schubert M, Lindgreen S, Orlando L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res Notes. 2016;9:88.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.

    Article  Google Scholar 

  52. Kampmann M, Horlbeck MA, Chen Y, Tsai JC, Bassik MC, Gilbert LA, et al. Next-generation libraries for robust RNA interference-based genome-wide screens. Proc Natl Acad Sci USA. 2015;112:E3384–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Morgens DW, Wainberg M, Boyle EA, Ursu O, Araya CL, Tsui CK, et al. Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nat Commun. 2017;8:15178.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kong AT, Leprevost FV, Avtonomov DM, Mellacheruvu D, Nesvizhskii AI. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods. 2017;14:513–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. FragPipe [Internet]. [cited 9 Sep 2023]. FragPipe. Available from: https://fragpipe.nesvilab.org/.

  57. da Veiga Leprevost F, Haynes SE, Avtonomov DM, Chang HY, Shanmugam AK, Mellacheruvu D, et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat Methods. 2020;17:869–70.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Schilling B, Rardin MJ, MacLean BX, Zawadzka AM, Frewen BE, Cusack MP, et al. Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline. Mol Cell Proteom. 2012;11:202–14.

    Article  CAS  Google Scholar 

  59. Choi M, Chang CY, Clough T, Broudy D, Killeen T, MacLean B, et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics. 2014;30:2524–6.

    Article  CAS  PubMed  Google Scholar 

  60. Zhang X, Smits AH, van Tilburg GB, Ovaa H, Huber W, Vermeulen M. Proteome-wide identification of ubiquitin interactions using UbIA-MS. Nat Protoc. 2018;13:530–50.

    Article  CAS  PubMed  Google Scholar 

  61. Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50:D543–52.

    Article  CAS  PubMed  Google Scholar 

  62. Riege K, Kretzmer H, Sahm A, McDade SS, Hoffmann S, Fischer M. Dissecting the DNA binding landscape and gene regulatory network of p63 and p53. Lal A, Murphy ME, Bourdon JC, editors. eLife. 2020;9:e63266.

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Acknowledgements

We thank Julien Sage for critical reading of the manuscript and Sydney Lu, Scott Dixon, and Camila Bolle for important discussions. We thank Mingxin Gu for her assistance with the in vivo screens. We thank Nitin Raj, Sofia Ferreira, and Kathryn Hanson for advice on RNA-sequencing. We thank Richard Frock for the use of his transilluminator and SpeedVac and Laura Andrejka for assistance with Tuba-seqUltra. We apologize to those whose work we could not cite due to spatial constraints.

Funding

This work was supported by Tobacco-Related Disease Research Program (TRDRP) fellowship T31DT1713, NIH T32CA009302 to AMB, NIH-R01CA234349 to DAP and MMW, and NIH R35CA197591 and TRDRP grant 28IP-0037 to LDA.

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Authors and Affiliations

  1. Division of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA

    Anthony M. Boutelle, Aicha R. Mabene, Mengxiong Wang, Edel M. McCrea, Liz J. Valente & Laura D. Attardi

  2. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    David Yao, Haiqing Xu, Yuning J. Tang, Steven S. Lopez, Monte M. Winslow, Michael C. Bassik & Laura D. Attardi

  3. Department of Biology, Stanford University, Stanford, CA, USA

    Haiqing Xu, Yuning J. Tang & Dmitri A. Petrov

  4. Department of Bioengineering, Stanford University Schools of Engineering and Medicine, Stanford, CA, USA

    Sauradeep Sinha & Fan Yang

  5. Baxter Laboratory, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Janos Demeter, Ran Cheng & Peter K. Jackson

  6. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Ran Cheng, Peter K. Jackson & Monte M. Winslow

  7. Division of Hematology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Brooks A. Benard & Ravindra Majeti

  8. Heligenics Inc, Las Vegas, NV, USA

    Liz J. Valente

  9. BioMed X Institute (GmbH), Heidelberg, Germany

    Alexandros P. Drainas

  10. Computational Biology Group, Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany

    Martin Fischer

  11. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Ravindra Majeti, Peter K. Jackson, Monte M. Winslow, Michael C. Bassik & Laura D. Attardi

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Contributions

AMB and LDA designed experiments and interpreted the results. AMB performed experiments and analyzed data. ARM and EMM conducted experiments. DY conducted casTLE analysis on sgRNA screens. HX, MW, YJT, and SSL performed Tuba-seqUltra pipeline. JD and RC processed and analyzed shotgun proteomics samples. LJV designed the sgRNA library. BAB performed analyses of DepMap data. SS performed and analyzed 3D migration experiments. MF conducted meta-analysis of p53-dependent gene expression and ChIP-seq binding. FY helped interpret 3D migration experiments. RM helped interpret DepMap data. PKJ helped interpret shotgun proteomics data. DAP and MMW helped interpret Tuba-seqUltra data. MCB helped interpret sgRNA screen results. AMB and LDA wrote the manuscript.

Corresponding author

Correspondence to Laura D. Attardi.

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Competing interests

RM is on the Advisory Boards of Kodikaz Therapeutic Solutions, Orbital Therapeutics, Pheast Therapeutics, 858 Therapeutics, Prelude Therapeutics, Mubadala Capital, and Aculeus Therapeutics. RM is a co-founder and equity holder of Pheast Therapeutics, MyeloGene, and Orbital Therapeutics. The other authors declare no competing interest.

Ethics

All animal experiments were performed in accordance with the Stanford University Administrative Panel on Laboratory Animal Care (protocol number 10382) guidelines and regulations. Mice (Mus musculus) were maintained at Stanford University’s Comparative Medicine Pavilion and Research Animal Facility according to practices prescribed by the National Institutes of Health and the Institutional Animal Care and Use Committee (IACUC). The Association for Assessment and Accreditation of Laboratory Animal Care provides additional accreditation to Stanford University.

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Boutelle, A.M., Mabene, A.R., Yao, D. et al. Integrative multiomic approaches reveal ZMAT3 and p21 as conserved hubs in the p53 tumor suppression network. Cell Death Differ (2025). https://doi.org/10.1038/s41418-025-01513-8

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