Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Protein-level mutant p53 reporters identify druggable rare precancerous clones in noncancerous tissues

Nature Cancer volume 4, pages 1176–1192 (2023)Cite this article

Subjects

Abstract

Detecting and targeting precancerous cells in noncancerous tissues is a major challenge for cancer prevention. Massive stabilization of mutant p53 (mutp53) proteins is a cancer-specific event that could potentially mark precancerous cells, yet in vivo protein-level mutp53 reporters are lacking. Here we developed two transgenic protein-level mutp53 reporters, p53R172H–Akaluc and p53–mCherry, that faithfully mimic the dynamics and function of mutp53 proteins in vivo. Using these reporters, we identified and traced rare precancerous clones in deep noncancerous tissues in various cancer models. In classic mutp53-driven thymic lymphoma models, we found that precancerous clones exhibit broad chromosome number variations, upregulate precancerous stage-specific genes such as Ybx3 and enhance amino acid transport and metabolism. Inhibiting amino acid transporters downstream of Ybx3 at the early but not late stage effectively suppresses tumorigenesis and prolongs survival. Together, these protein-level mutp53 reporters reveal undercharacterized features and vulnerabilities of precancerous cells during early tumorigenesis, paving the way for precision cancer prevention.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mutp53 reporters faithfully mimic the dynamics and in vivo function of hotspot mutp53 proteins.
Fig. 2: p53R172H–Akaluc monitors tumorigenesis of various cancers from early lesions.
Fig. 3: p53–mCherry marks precancerous cells in noncancerous tissues.
Fig. 4: scRNA-seq reveals the clonal and subclonal architecture and molecular characteristics of mutp53-stabilizing precancerous and cancerous cells.
Fig. 5: Preventative inhibition of YBX3–LAT1/CD98 suppresses tumorigenesis from precancerous lesions.
Fig. 6: scRNA-seq reveals potential mechanisms for JPH203 resistance in precancerous samples.

Similar content being viewed by others

Data availability

Single-cell and bulk RNA-seq data that support the findings of this study have been deposited in the Genome Sequence Archive at the National Genomics Data Center (NGDC) under accession codes CRA006353, CRA009287 and CRA009474 (https://ngdc.cncb.ac.cn/gsa). The TCRβ sequencing data with detailed quality control information, mass spectrometry data and processed Seurat objects for scRNA-seq, including the expression matrix and cell annotation information, are available on FigShare (https://doi.org/10.6084/m9.figshare.21901545). Public bulk RNA-seq data for p53-mutant and p53-null thymic lymphomas used in this study are available in the Gene Expression Omnibus (GEO) under accession codes GSE60827, SRP166766 and PRJNA881746 (https://www.ncbi.nlm.nih.gov/gds). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The scripts used to perform the main analyses are available on FigShare (https://doi.org/10.6084/m9.figshare.21901545).

References

  1. Li, R. et al. A body map of somatic mutagenesis in morphologically normal human tissues. Nature 597, 398–403 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Kakiuchi, N. & Ogawa, S. Clonal expansion in non-cancer tissues. Nat. Rev. Cancer 21, 239–256 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Robles, A. I., Jen, J. & Harris, C. C. Clinical outcomes of TP53 mutations in cancers. Cold Spring Harb. Perspect. Med. https://doi.org/10.1101/cshperspect.a026294 (2016).

  6. Kocher, B. & Piwnica-Worms, D. Illuminating cancer systems with genetically engineered mouse models and coupled luciferase reporters in vivo. Cancer Discov. 3, 616–629 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Burd, C. E. et al. Monitoring tumorigenesis and senescence in vivo with a p16INK4a-luciferase model. Cell 152, 340–351 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lang, G. A. et al. Gain of function of a p53 hot spot mutation in a mouse model of Li–Fraumeni syndrome. Cell 119, 861–872 (2004).

    Article  CAS  PubMed  Google Scholar 

  9. Hanel, W. et al. Two hot spot mutant p53 mouse models display differential gain of function in tumorigenesis. Cell Death Differ. 20, 898–909 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Olive, K. P. et al. Mutant p53 gain of function in two mouse models of Li–Fraumeni syndrome. Cell 119, 847–860 (2004).

    Article  CAS  PubMed  Google Scholar 

  11. Muller, P. A. & Vousden, K. H. Mutant p53 in cancer: new functions and therapeutic opportunities. Cancer Cell 25, 304–317 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mantovani, F., Collavin, L. & Del Sal, G. Mutant p53 as a guardian of the cancer cell. Cell Death Differ. 26, 199–212 (2019).

    Article  PubMed  Google Scholar 

  13. Terzian, T. et al. The inherent instability of mutant p53 is alleviated by Mdm2 or p16INK4a loss. Genes Dev. 22, 1337–1344 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Suh, Y. A. et al. Multiple stress signals activate mutant p53 in vivo. Cancer Res. 71, 7168–7175 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Alexandrova, E. M. et al. Improving survival by exploiting tumour dependence on stabilized mutant p53 for treatment. Nature 523, 352–356 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang, Y. et al. Expression of mutant p53 proteins implicates a lineage relationship between neural stem cells and malignant astrocytic glioma in a murine model. Cancer Cell 15, 514–526 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Berg, R. J. et al. Early p53 alterations in mouse skin carcinogenesis by UVB radiation: immunohistochemical detection of mutant p53 protein in clusters of preneoplastic epidermal cells. Proc. Natl Acad. Sci. USA 93, 274–278 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang, L. D., Hong, J. Y., Qiu, S. L., Gao, H. & Yang, C. S. Accumulation of p53 protein in human esophageal precancerous lesions: a possible early biomarker for carcinogenesis. Cancer Res. 53, 1783–1787 (1993).

    CAS  PubMed  Google Scholar 

  19. Kubo, Y. et al. p53 gene mutations in human skin cancers and precancerous lesions: comparison with immunohistochemical analysis. J. Invest. Dermatol. 102, 440–444 (1994).

    Article  CAS  PubMed  Google Scholar 

  20. Humpton, T. J. et al. A noninvasive iRFP713 p53 reporter reveals dynamic p53 activity in response to irradiation and liver regeneration in vivo. Sci. Signal. 15, eabd9099 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Murai, K. et al. Epidermal tissue adapts to restrain progenitors carrying clonal p53 mutations. Cell Stem Cell 23, 687–699 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Iwano, S. et al. Single-cell bioluminescence imaging of deep tissue in freely moving animals. Science 359, 935–939 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Takaoka, A. et al. Integration of interferon-α/β signalling to p53 responses in tumour suppression and antiviral defence. Nature 424, 516–523 (2003).

    Article  CAS  PubMed  Google Scholar 

  24. Liu, G. et al. High metastatic potential in mice inheriting a targeted p53 missense mutation. Proc. Natl Acad. Sci. USA 97, 4174–4179 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Harvey, M., McArthur, M. J., Montgomery, C. A. Jr., Bradley, A. & Donehower, L. A. Genetic background alters the spectrum of tumors that develop in p53-deficient mice. FASEB J. 7, 938–943 (1993).

    Article  CAS  PubMed  Google Scholar 

  26. Kuperwasser, C. et al. Development of spontaneous mammary tumors in BALB/c p53 heterozygous mice. A model for Li–Fraumeni syndrome. Am. J. Pathol. 157, 2151–2159 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Georgiades, P. et al. VavCre transgenic mice: a tool for mutagenesis in hematopoietic and endothelial lineages. Genesis 34, 251–256 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Zhuo, L. et al. hGFAP-cre transgenic mice for manipulation of glial and neuronal function in vivo. Genesis 31, 85–94 (2001).

    Article  CAS  PubMed  Google Scholar 

  29. Marino, S., Vooijs, M., van Der Gulden, H., Jonkers, J. & Berns, A. Induction of medulloblastomas in p53-null mutant mice by somatic inactivation of Rb in the external granular layer cells of the cerebellum. Genes Dev. 14, 994–1004 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Li, Y. et al. Murine models of IDH-wild-type glioblastoma exhibit spatial segregation of tumor initiation and manifestation during evolution. Nat. Commun. 11, 3669 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Xue, Y., San Luis, B. & Lane, D. P. Intratumour heterogeneity of p53 expression; causes and consequences. J. Pathol. 249, 274–285 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Kozar, S. et al. Continuous clonal labeling reveals small numbers of functional stem cells in intestinal crypts and adenomas. Cell Stem Cell 13, 626–633 (2013).

    Article  CAS  PubMed  Google Scholar 

  33. Lu, Q. R. et al. Oligodendrocyte lineage genes (OLIG) as molecular markers for human glial brain tumors. Proc. Natl Acad. Sci. USA 98, 10851–10856 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Dudgeon, C. et al. The evolution of thymic lymphomas in p53 knockout mice. Genes Dev. 28, 2613–2620 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Li, Y. et al. Development of double-positive thymocytes at single-cell resolution. Genome Med. 13, 49 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kernfeld, E. M. et al. A single-cell transcriptomic atlas of thymus organogenesis resolves cell types and developmental maturation. Immunity 48, 1258–1270 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Klein, A. M., de Queiroz, R. M., Venkatesh, D. & Prives, C. The roles and regulation of MDM2 and MDMX: it is not just about p53. Genes Dev. 35, 575–601 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Silva, A. et al. Overexpression of wild-type IL-7Rα promotes T-cell acute lymphoblastic leukemia/lymphoma. Blood 138, 1040–1052 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cooke, A. et al. The RNA-binding protein YBX3 controls amino acid levels by regulating SLC mRNA abundance. Cell Rep. 27, 3097–3106 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Krige, D. et al. CHR-2797: an antiproliferative aminopeptidase inhibitor that leads to amino acid deprivation in human leukemic cells. Cancer Res. 68, 6669–6679 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Bianchi, J. J., Murigneux, V., Bedora-Faure, M., Lescale, C. & Deriano, L. Breakage-fusion-bridge events trigger complex genome rearrangements and amplifications in developmentally arrested T cell lymphomas. Cell Rep. 27, 2847–2858 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Hwang, S. M. et al. LCK-mediated RIPK3 activation controls double-positive thymocyte proliferation and restrains thymic lymphoma by regulating the PP2A–ERK axis. Adv. Sci. 9, e2204522 (2022).

    Article  Google Scholar 

  45. Venkatanarayan, A. et al. IAPP-driven metabolic reprogramming induces regression of p53-deficient tumours in vivo. Nature 517, 626–630 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Okano, N. et al. First-in-human phase I study of JPH203, an L-type amino acid transporter 1 inhibitor, in patients with advanced solid tumors. Invest. New Drugs 38, 1495–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

  47. Wempe, M. F. et al. Metabolism and pharmacokinetic studies of JPH203, an L-amino acid transporter 1 (LAT1) selective compound. Drug Metab. Pharmacokinet. 27, 155–161 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. McCabe, M. T. et al. EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations. Nature 492, 108–112 (2012).

    Article  CAS  PubMed  Google Scholar 

  49. Jackson, J. G. & Lozano, G. The mutant p53 mouse as a pre-clinical model. Oncogene 32, 4325–4330 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Kastenhuber, E. R. & Lowe, S. W. Putting p53 in context. Cell 170, 1062–1078 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Baslan, T. et al. Ordered and deterministic cancer genome evolution after p53 loss. Nature 608, 795–802 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Jain, A. K. & Barton, M. C. p53: emerging roles in stem cells, development and beyond. Development https://doi.org/10.1242/dev.158360 (2018).

  53. Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Tan, Y. S. & Lei, Y. L. Generation and culture of mouse embryonic fibroblasts. Methods Mol. Biol. 1960, 85–91 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wang, X. et al. Sequential fate-switches in stem-like cells drive the tumorigenic trajectory from human neural stem cells to malignant glioma. Cell Res. 31, 684–702 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wang, S., Song, P. & Zou, M. H. Inhibition of AMP-activated protein kinase α (AMPKα) by doxorubicin accentuates genotoxic stress and cell death in mouse embryonic fibroblasts and cardiomyocytes: role of p53 and SIRT1. J. Biol. Chem. 287, 8001–8012 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gong, Y. et al. PUMILIO proteins promote colorectal cancer growth via suppressing p21. Nat. Commun. 13, 1627 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Bhaduri, A. et al. Outer radial glia-like cancer stem cells contribute to heterogeneity of glioblastoma. Cell Stem Cell 26, 48–63 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Y. Zhu at Children’s National Medical Center (Washington, DC) for providing the Nf1flox mice and other breeder mice; T. Jacks at the Massachusetts Institute of Technology for the Trp53LSL-R172H mice; A. Berns at the University of Amsterdam for the Trp53flox mice; A. Messing at the University of Wisconsin–Madison for the hGFAP-Cre mice; A. Miyawaki and S. Iwano at the Brain Science Institute, RIKEN, for Akaluc plasmid information; C. Liu, K. Lu, H. Jiang and X. He for constructive discussions; and B. Chen for technical assistance. This work was supported by the National Key Research and Development Program of China, Stem Cell and Translational Research (2022YFA1105200 to Y.W.), the National Natural Science Foundation of China (82273117 to Y.W., 32000554 to P.Y. and 82173179 to Y.Z.), the Distinguished Young Scientists Program of Sichuan Province (2019JDJQ0029 to Y.W.) and the West China Hospital of Sichuan University under awards from the 1·3·5 project for disciplines of excellence (ZYYC20019 to Y.W.), the Post-Doctor Research Project (19HXBH010 to P.Y.) and the National Clinical Research Center for Geriatrics (Z2021JC006 to Y.Z.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Pengle Yao, Peng Xiao, Zongyao Huang.

Authors and Affiliations

  1. Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China

    Pengle Yao, Peng Xiao, Zongyao Huang, Min Tang, Xiwen Tang, Gaoxia Yang, Qi Zhang, Xinpei Li, Zhengnan Yang, Chuanxing Xie, Yutong Liu, Chong Chen, Yu Liu & Yuan Wang

  2. National Clinical Research Center for Geriatrics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China

    Hui Gong, Xiuxuan Wang, Lunzhi Dai, Hengyi Xiao & Yan Zhang

  3. Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University, Chengdu, China

    Guihua Wang, Da Jia & Lu Chen

  4. Core Facilities of West China Hospital, Sichuan University, Chengdu, China

    Huifang Li

Authors
  1. Pengle Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zongyao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Min Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiwen Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gaoxia Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xinpei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zhengnan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Chuanxing Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hui Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Guihua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yutong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Xiuxuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Huifang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Da Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Lunzhi Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Lu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Chong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Hengyi Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Yan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Yuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.W. conceived the study, supervised the project, analyzed the data and wrote the manuscript. Y.Z. supervised and analyzed the flow cytometry experiments. P.Y. and P.X., assisted by X.T., X.L., Z.Y., H.G., G.W. and H.L., performed mouse breeding and monitoring, histology, immunofluorescence staining and flow cytometry analyses. P.Y., P.X., Q.Z. and X.T. performed cellular imaging or live imaging experiments and helped with manuscript preparation. Z.H. performed the bioinformatic analyses and helped with manuscript preparation. P.X. and C.X., assisted by P.Y., performed scRNA-seq. X.W. and P.X., assisted by X.L., performed mass spectrometry and data analysis. P.Y., assisted by M.T., G.Y. and Yutong Liu, performed the MEF experiments. D.J., L.D., C.C., Yu Liu, L.C. and H.X. provided key experimental resources and critically reviewed the manuscript.

Corresponding author

Correspondence to Yuan Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Guillermina Lozano, Thorsten Zenz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Validation of Trp53 mCherry and Trp53 R172H-Akaluc alleles.

(a) Genotyping PCR for Trp53 wildtype, PAL/ + , PAL/PAL mice using F2/R2/R3 primers in Fig. 1a. 240 bp, wildtype band. 416 bp, mutant band. Results are representative of at least 80 independent genotyping experiments. (b) Southern blot of EcoNI- or ScaI-digested tail DNA from Trp53 PAL/+ mice using 5’ probe or RR probes in Fig. 1a. Trp53 PAL shows specific 11.1 kb or 3.5 kb bands, respectively. Results are representative of 8 independent experiments. (c) Genotyping PCR for Trp53 mCherry/+ mice using CF1/R1 or CF2/R2 primers in Fig. 1a. Trp53 wildtype mouse was used as a control, showing no band. Results are representative of at least 80 independent genotyping experiments. (d) Southern blot of EcoNI-digested tail DNA from Trp53 mCherry/+ mice using 5’ probe or RR probes in Fig. 1a. Trp53 mCherry shows specific 5.4 kb or 4.5 kb bands, respectively. Results are representative of 5 independent experiments. (e) Targeted Sanger sequencing for PCR products from Trp53 PAL/+ tail DNA using F1 or F2/R3 primers in Fig. 1a, highlighting the G to A mutation at the R172H site, and the junction sequences between exon 11, linker, Akaluc, and 3’ UTR.

Extended Data Fig. 2 Performance of the p53R172H-Akaluc reporter for cellular and in vivo bioluminescence imaging.

(a) qPCR (left) and Western blot (right) comparing the RNA and protein expression of p53R172H-Akaluc and p53R172H-Luc Jurkat cells, normalized to p53R172H-Luc levels. P values, two-tailed t test. Data were based on three technical replicates per group and shown as mean ± SEM, representing three independent experiments. (b) Cellular imaging of 10,000 p53R172H-Akaluc or p53R172H-Luc Jurkat cells per well, using different enzyme/substrate combinations: Akaluc/Luciferin, Akaluc/AkaLumine, Luc/Luciferin, and Luc/AkaLumine. (c) Cellular imaging of 1000 or 10,000 p53R172H-Akaluc or p53R172H-Luc Jurkat cells per well using optimal enzyme/substrate combinations. Expression-normalized bioluminescence of p53-Akaluc/AkaLumine and p53R172H-Luc/Luciferin was quantified and compared, normalized to the level of 1000 cells per well p53R172H-Luc/Luciferin. P values, two-tailed t test. Data were based on four biological repeats per group and shown as mean ± SEM. (d) Representative in vivo imaging of 100,000 i.v. injected p53R172H-Akaluc (n = 4 mice) or p53R172H-Luc (n = 3 mice) Jurkat cells trapped in the lung of nude mice, quantified and compared at the bottom, normalized to the level of the p53R172H-Luc group. P values, two-tailed t test. Data were shown as mean ± SEM. (e) Representative in vivo imaging of 50 (n = 2 mice), 100 (n = 4 mice), 500 (n = 2 mice), 1000 (n = 1 mouse) i.v. injected p53R172H-Akaluc Jurkat cells trapped in the lung of nude mice. Standard curve was calculated at the bottom, and the correlation R2 is shown. Data points represent mean values for each group.

Source data

Extended Data Fig. 3 Endstage primary tumors and metastasis in p53-mutant mice.

(a) Whole-mount images of thymic lymphoma, carcinoma, and sarcoma from PCC mice. (b) Whole-mount images of spleen and liver metastasis in PCL and PLL mice. (c) Representative H&E staining of sarcoma (n = 4 tumors) metastasized to the spleen or liver, and carcinoma (n = 3 tumors) metastasized to the liver in PCL and PLL mice. (d) An example H&E staining of multi-focal malignant glioma in PCN and PCLN mice. (e) Representative FACS analyses of mCherry on thymic lymphoma, carcinoma, and sarcoma from PCC mice, using thymuses from age-matched wildtype mice as controls. The boxed area shows the gating strategy and the percentage of p53mCherry+ cells. Results are representative of 3 samples per tumor type.

Extended Data Fig. 4 Mutp53 reporters are expressed as fusion proteins in vivo.

(a) Live imaging of wildtype and Trp53 PAL/PAL mice before and 24 h after 8 Gy X-rays irradiation. (b) Western blot for p53 and Gapdh in the whole spleen lysate from three Trp53 PAL/PAL mice with or without 8 Gy X-rays. Spleen lysate from a wildtype mouse was used as a control. Similar results were obtained from two independent experiments. (c) Mass spectrometry of PCL thymic lymphoma lysate. Trypsin, cleavage sites in wildtype p53 and p53-PAL peptide sequences. Red capitalized letters, the linker peptide sequence. Blue capitalized letters, the Akaluc N-terminal sequence. (d) Western blot for p53 and mCherry in thymic lymphoma lysate from two PCV mice. Glioblastoma (GBM) lysate from a Trp53 R172H/flox; hGFAP-Cre+ mouse was used as a control. Similar results were obtained from three independent experiments. (e) Mass spectrometry for PCV thymic lymphoma lysate. Trypsin, trypsin cleavage sites in wildtype p53 and p53mCherry peptide sequences. Red capitalized letters, the N-terminal sequence of mCherry.

Source data

Extended Data Fig. 5 Mutp53 reporter signals in the abdomen and precancerous brain.

(a) Live imaging of 2 M PCL mice without any sign of tumor development across five generations. (b) Ex vivo bioluminescence imaging of freshly dissected intestine, colon, liver, and spleen from a 2 M mouse. (c) Total luminescence in the abdominal area of PCL mice at 2 M across five generations was quantified and compared. One-way ANOVA test was performed. No statistically significant difference among groups. Data were shown as mean ± SEM, n = 14, 7, 9, 5, 5 mice per group. (d) Total luminescence in the abdominal area of PCL mice at 3 M (n = 13 mice), 4 M (n = 9 mice), and 5 M (n = 5 mice) from the same generation was quantified and compared. One-way ANOVA test was performed. No statistically significant difference among groups. Data were shown as mean ± SEM. (e) Representative immunofluorescence (IF) co-labeling of p53 and mCherry in PCN brains (n = 3 mice). Dashed boxes mark a p53+mCherry+ precancerous cell cluster in the olfactory bulb from a PCN mouse without brain tumors, highlighted in the high-magnification images (last row). CTX, the cerebral cortex. OB, olfactory bulb. Arrows, co-labeled cells. Scale bars, 100 μm. (f) Representative IF co-labeling for Olig2 and mCherry (n = 3 mice) in precancerous cells inside the olfactory bulb from the same PCN mouse in (e). Arrows, co-labeled cells. Arrowheads, Olig2+mCherry− oligodendrocyte lineage cells. Scale bars, 100 μm.

Source data

Extended Data Fig. 6 FACS plots and cell-type composition of individual scRNA-seq samples.

(a) FACS plots for each sample used for single-cell RNA sequencing, using thymuses from age-matched wildtype mice as controls. The boxed area shows the gating strategy and the percentage of p53mCherry+ cells. (b) Dot plot showing expression of marker genes (rows) among different cell types (columns), n = 10 samples. The size of each dot represents the percentage of cells expressing a given marker. The red intensity of each dot represents scaled average gene expression. (c) Cells from individual samples (n = 10) are shown in UMAP scatter plots based on integrated clustering in Fig. 4b. Colors distinguish cell types. Cell#, the total number of high-quality cells in each sample used for subsequent analysis. N, p53mCherry− cells. P, p53mCherry+ cells. (d) The cell-type composition among different samples (n = 10). ISP G2M and G1/S cells were grouped together as cycling ISP. CD4 or CD8 single-positive T cells were grouped together as SP. Cells not belonging to the T-cell lineage were grouped as Non-T.

Extended Data Fig. 7 The clonal/subclonal architecture of mutp53-stabilizing precancerous and cancerous cells.

(a, b, c) InferCNV analyses on single-cells from 4 M (a, n = 3251 cells), 5 M (b, n = 5105 cells), and End2 (c, n = 7292 cells), combining mCherry+ and mCherry− cells from the same mice and using cells from WT as a reference. Yellow and green lines on the left distinguish mCherry+ and mCherry−cells. Blue, chromosome loss. Red, chromosome gain. Dashed boxes mark different clones or subclones. (a’, b’, c’) Reconstructed clonal evolutionary trees for clones/subclones marked in (a, b, c) based on the most parsimonious order of CNV occurrences. For cells at each arm of clonal evolutionary trees, their total number, mCherry+ cell percentage (red%), shared CNVs (Amp, amplification; Del, deletion/loss), and cell type composition (stacked bars) are indicated.

Extended Data Fig. 8 Deregulated pathways in precancerous ISPs.

(a) Venn charts showing the overlap of genes upregulated and downregulated in p53mCherry+ ISPs cells compared with paired mCherry− ISPs without overt CNVs in 3M_C1 and 4M_C1 clones. (b) GSEA analyses of shared differentially expressed genes in (a). Top tumor-related pathways and KRIGE_RESPONSE_TO_TOSEDOSTAT_24HR_DN pathway (genes down-regulated upon aminopeptidase inhibitor Tosedostat treatment) are shown (P adj. < 0.01). P values and NES scores were calculated using GSEA algorithm (Methods).

Extended Data Fig. 9 Potential impact of Mdm2 and Ybx3 on mutp53 stabilization.

(a) Violin plots of scaled Mdm2 expression in cycling ISPs (G2/M and G1/S) from different samples grouped by their clonal/subclonal identities and p53mCherry positivity. The median Mdm2 expression levels for each group of cells are shown at the bottom. n = 475, 575, 191, 100, 324, 81, 71, 3310, and 10141 cells, respectively. P values, two-tailed Kruskal–Wallis test for between group differences with Holm’s correction for multiple comparisons. (b) The expression of Ybx3 in WT, PCC, and PLL MEFs quantified by qPCR, normalized to WT. Mean Cq values for each group are shown. One-way ANOVA test was performed. Data were based on two technical replicates per group and shown as mean ± SEM, representing three independent experiments. (c) The expression of Ybx3 in non-transfected PCC MEFs (Blank) and MEFs transfected with shCTR or shYbx3-1-4 by qPCR, normalized to the blank group. One-way ANOVA test was performed. No statistically significant difference among groups. Data were based on two technical replicates per group and shown as mean ± SEM, representing three independent experiments. (d) Western blot for p53mCherry or p53R172H-Akaluc in PCC and PLL MEFs transfected with shCTR or shYbx3-1-4. (e, e’) Bulk RNA-seq of p53 KO/KO thymic lymphoma samples (from Bianchi et al., Hwang et al., and Venkatanarayan et al.) and PCL/PCV p53-mutant thymic lymphomas (FACS-sorted into mCherry+ and mCherry− samples) were analyzed and compared. Box plots show adjusted TPM values of Slc3a2, Slc7a5, and signature scores of amino acid transport or amino acid metabolism genesets. Boxes indicate quartiles, horizontal bar indicates median, and whiskers indicate range, up to 1.5-fold inter-quartile range. n, sample number.

Source data

Extended Data Fig. 10 Single-cell RNA-seq profiling of p53mCherry+ JPH203-resistant cells.

(a) High quality single cells from two p53mCherry+ JPH203-resistant samples collect at 2.5 M or 3 M of age (JR2.5 M or JR3M) were integrated with single cells shown in Fig. 4b by Harmony for cell clustering and annotation. Cells from individual samples are shown in UMAP scatter plots. Colors distinguish cell types. ISP G2M and G1/S cells were grouped together as cycling ISPs. n = 10417 and 11580 cells, respectively. (b) The cell-type composition among the two samples in (A). Cell#, the total number of high-quality cells in each sample. CD4 or CD8 single-positive T cells were grouped together as SP. Non-T, non-T cell lineage cells. (c) Violin plots of scaled Mki67 expression in cycling ISPs from different clones/subclones in WT, treatment-naïve, and treatment-resistant samples. ***, P adj. < 0.001, two-tailed Kruskal–Wallis test for between group differences with Holm’s correction for multiple comparisons. (d) Cycling ISPs from resistant samples (JR2.5 M or JR3M) were compared with those from treatment-naïve precancerous samples. Genes commonly upregulated or downregulated in ISPs from both resistant samples were subjected to GSEA analysis (P adj. < 0.01). P values and NES scores were calculated using GSEA algorithm (Methods).

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7.

Source data

Source Data Fig. 1

Unprocessed western blots.

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 2

Unprocessed western blots.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 9

Unprocessed western blots.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, P., Xiao, P., Huang, Z. et al. Protein-level mutant p53 reporters identify druggable rare precancerous clones in noncancerous tissues. Nat Cancer 4, 1176–1192 (2023). https://doi.org/10.1038/s43018-023-00608-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-023-00608-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer