Early TP53 alterations engage environmental exposures to promote gastric premalignancy in an integrative mouse model

Abstract

Somatic alterations in cancer genes are being detected in normal and premalignant tissue, thus placing greater emphasis on gene–environment interactions that enable disease phenotypes. By combining early genetic alterations with disease-relevant exposures, we developed an integrative mouse model to study gastric premalignancy. Deletion of Trp53 in gastric cells confers a selective advantage and promotes the development of dysplasia in the setting of dietary carcinogens. Organoid derivation from dysplastic lesions facilitated genomic, transcriptional and functional evaluation of gastric premalignancy. Cell cycle regulators, most notably Cdkn2a, were upregulated by p53 inactivation in gastric premalignancy, serving as a barrier to disease progression. Co-deletion of Cdkn2a and Trp53 in dysplastic gastric organoids promoted cancer phenotypes but also induced replication stress, exposing a susceptibility to DNA damage response inhibitors. These findings demonstrate the utility of mouse models that integrate genomic alterations with relevant exposures and highlight the importance of gene–environment interactions in shaping the premalignant state.

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Fig. 1: Lgr5-p53KO promotes dysplastic and metaplastic premalignant lesions in the setting of dietary carcinogen exposure.
Fig. 2: p53KO human non-dysplastic premalignant cells have a selective advantage in the setting of MNU exposure.
Fig. 3: p53KO murine gastric organoids have a competitive advantage in the setting of dietary carcinogenesis.
Fig. 4: Dysplastic and metaplastic Lgr5-p53KO gastric organoids demonstrate genome doubling and capacity to transform.
Fig. 5: Dysregulation of inflammatory, stem cell and cell cycle pathways in dysplastic Lgr5-p53KO gastric organoids.
Fig. 6: CDKN2A/p16 is upregulated in Lgr5-p53KO premalignant gastric lesions and co-altered with TP53 in a subset of human gastric cancers.
Fig. 7: Co-deletion of CDKN2A/p16 and TP53 promotes gastric premalignancy, induces replication stress and sensitizes to DDR pathway blockade.

Data availability

All requests for raw and analyzed data and materials should be directed to the corresponding authors. Data and materials that can be shared will be released via a material transfer agreement. Raw and processed RNA sequencing data have been deposited online with the accession number GSE141625. Raw genome sequencing data have been deposited online with the accession numbers PRJNA594147 and PRJNA594086. A list of significantly mutated genes from WES is available in Supplementary Table 1. Source data for Figs. 2, 5 and 7 and Extended Data Figs. 3 and 4 are presented with the paper.

References

  1. 1.

    Wagner, A. D. et al. Chemotherapy for advanced gastric cancer. Cochrane Database Syst. Rev. 8, CD004064 (2017).

  2. 2.

    Yang, K. et al. Dietary modulation of carcinoma development in a mouse model for human familial adenomatous polyposis. Cancer Res. 58, 5713–5717 (1998).

  3. 3.

    Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015).

  4. 4.

    The Cancer Genome Atlas Research Network Integrated genomic characterization of oesophageal carcinoma. Nature 541, 169–175 (2017).

  5. 5.

    Quante, M. et al. Bile acid and inflammation activate gastric cardia stem cells in a mouse model of Barrett-like metaplasia. Cancer Cell 21, 36–51 (2012).

  6. 6.

    Giroux, V. & Rustgi, A. K. Metaplasia: tissue injury adaptation and a precursor to the dysplasia-cancer sequence. Nat. Rev. Cancer 17, 594–604 (2017).

  7. 7.

    Chen, S. L., Mo, J. Z., Cao, Z. J., Chen, X. Y. & Xiao, S. D. Effects of bile reflux on gastric mucosal lesions in patients with dyspepsia or chronic gastritis. World J. Gastroenterol. 11, 2834–2837 (2005).

  8. 8.

    Sobala, G. M. et al. Bile reflux and intestinal metaplasia in gastric mucosa. J. Clin. Pathol. 46, 235–240 (1993).

  9. 9.

    Stefaniwsky, A. B., Tint, G. S., Speck, J., Shefer, S. & Salen, G. Ursodeoxycholic acid treatment of bile reflux gastritis. Gastroenterology 89, 1000–1004 (1985).

  10. 10.

    Tomita, H. et al. Inhibition of gastric carcinogenesis by the hormone gastrin is mediated by suppression of TFF1 epigenetic silencing. Gastroenterology 140, 879–891 (2011).

  11. 11.

    Lijinsky, W. & Epstein, S. S. Nitrosamines as environmental carcinogens. Nature 225, 21–23 (1970).

  12. 12.

    Lijinsky, W. N-Nitroso compounds in the diet. Mutat. Res. 443, 129–138 (1999).

  13. 13.

    Ding, L., El Zaatari, M. & Merchant, J. L. Recapitulating human gastric cancer pathogenesis: experimental models of gastric cancer. Adv. Exp. Med. Biol. 908, 441–478 (2016).

  14. 14.

    Petersen, C. P., Mills, J. C. & Goldenring, J. R. Murine models of gastric corpus preneoplasia. Cell Mol. Gastroenterol. Hepatol. 3, 11–26 (2017).

  15. 15.

    Yamachika, T. et al. N-methyl-N-nitrosourea concentration-dependent, rather than total intake-dependent, induction of adenocarcinomas in the glandular stomach of BALB/c mice. Jpn J. Cancer Res. 89, 385–391 (1998).

  16. 16.

    Fox, J. G. et al. Germ-line p53-targeted disruption inhibits Helicobacter-induced premalignant lesions and invasive gastric carcinoma through down-regulation of Th1 proinflammatory responses. Cancer Res. 62, 696–702 (2002).

  17. 17.

    Wang, T. C. et al. Synergistic interaction between hypergastrinemia and Helicobacter infection in a mouse model of gastric cancer. Gastroenterology 118, 36–47 (2000).

  18. 18.

    Wang, T. C. et al. Mice lacking secretory phospholipase A2 show altered apoptosis and differentiation with Helicobacter felis infection. Gastroenterology 114, 675–689 (1998).

  19. 19.

    Hayakawa, Y. et al. Mist1 expressing gastric stem cells maintain the normal and neoplastic gastric epithelium and are supported by a perivascular stem cell niche. Cancer Cell 28, 800–814 (2015).

  20. 20.

    Hayakawa, Y., Fox, J. G. & Wang, T. C. The origins of gastric cancer from gastric stem cells: lessons from mouse models. Cell. Mol. Gastroenterol. Hepatol. 3, 331–338 (2017).

  21. 21.

    Sarkar, A. et al. Sox2 suppresses gastric tumorigenesis in mice. Cell Rep. 16, 1929–1941 (2016).

  22. 22.

    Leushacke, M. et al. Lgr5-expressing chief cells drive epithelial regeneration and cancer in the oxyntic stomach. Nat. Cell Biol. 19, 774–786 (2017).

  23. 23.

    Dulak, A. M. et al. Exome and whole-genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity. Nat. Genet. 45, 478–486 (2013).

  24. 24.

    Stachler, M. D. et al. Paired exome analysis of Barrett's esophagus and adenocarcinoma. Nat. Genet. 47, 1047–1055 (2015).

  25. 25.

    Zang, Z. J. et al. Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes. Nat. Genet. 44, 570–574 (2012).

  26. 26.

    Sperling, A. S., Gibson, C. J. & Ebert, B. L. The genetics of myelodysplastic syndrome: from clonal haematopoiesis to secondary leukaemia. Nat. Rev. Cancer 17, 5–19 (2017).

  27. 27.

    Steensma, D. P. et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood 126, 9–16 (2015).

  28. 28.

    Huang, K. K. et al. Genomic and epigenomic profiling of high-risk intestinal metaplasia reveals molecular determinants of progression to gastric cancer. Cancer Cell 33, 137–150.e5 (2018).

  29. 29.

    Maley, C. C. et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006).

  30. 30.

    Shimizu, T. et al. Accumulation of somatic mutations in TP53 in gastric epithelium with Helicobacter pylori infection. Gastroenterology 147, 407–17 e3 (2014).

  31. 31.

    Stachler, M. D. et al. Detection of mutations in Barrett's esophagus before progression to high-grade dysplasia or adenocarcinoma. Gastroenterology 155, 156–167 (2018).

  32. 32.

    Wang, L., Guo, H., Lin, C., Yang, L. & Wang, X. Enrichment and characterization of cancer stemlike cells from a cervical cancer cell line. Mol. Med. Rep. 9, 2117–2123 (2014).

  33. 33.

    Van de Peer, Y., Mizrachi, E. & Marchal, K. The evolutionary significance of polyploidy. Nat. Rev. Genet. 18, 411–424 (2017).

  34. 34.

    Mirvish, S. S. Role of N-nitroso compounds (NOC) and N-nitrosation in etiology of gastric, esophageal, nasopharyngeal and bladder cancer and contribution to cancer of known exposures to NOC. Cancer Lett. 93, 17–48 (1995).

  35. 35.

    Bulay, O. et al. Carcinogenicity test of six nitrosamides and a nitrosocyanamide administered orally to rats. J. Natl. Cancer Inst. 62, 1523–1528 (1979).

  36. 36.

    Tu, S. et al. Overexpression of interleukin-1beta induces gastric inflammation and cancer and mobilizes myeloid-derived suppressor cells in mice. Cancer Cell 14, 408–419 (2008).

  37. 37.

    Westcott, P. M. et al. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature 517, 489–492 (2015).

  38. 38.

    Barker, N. et al. Lgr5+ve stem cells drive self-renewal in the stomach and build long-lived gastric units in vitro. Cell Stem Cell 6, 25–36 (2010).

  39. 39.

    Goldenring, J. R., Nam, K. T. & Mills, J. C. The origin of pre-neoplastic metaplasia in the stomach: chief cells emerge from the Mist. Exp. Cell Res. 317, 2759–2764 (2011).

  40. 40.

    Bielski, C. M. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50, 1189–1195 (2018).

  41. 41.

    Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689 e3 (2018).

  42. 42.

    The Cancer Genome Atlas Research Network Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).

  43. 43.

    Liu, Y. et al. Comparative molecular analysis of gastrointestinal adenocarcinomas. Cancer Cell 33, 721–735.e8 (2018).

  44. 44.

    Galipeau, P. C. et al. 17p (p53) allelic losses, 4N (G2/tetraploid) populations, and progression to aneuploidy in Barrett's esophagus. Proc. Natl Acad. Sci. USA 93, 7081–7084 (1996).

  45. 45.

    Galipeau, P. C., Prevo, L. J., Sanchez, C. A., Longton, G. M. & Reid, B. J. Clonal expansion and loss of heterozygosity at chromosomes 9p and 17p in premalignant esophageal (Barrett’s) tissue. J. Natl. Cancer Inst. 91, 2087–2095 (1999).

  46. 46.

    Nanki, K. et al. Divergent routes toward Wnt and R-spondin niche independency during human gastric carcinogenesis. Cell 174, 856–869.e17 (2018).

  47. 47.

    Ross-Innes, C. S. et al. Whole-genome sequencing provides new insights into the clonal architecture of Barrett's esophagus and esophageal adenocarcinoma. Nat. Genet. 47, 1038–1046 (2015).

  48. 48.

    Jaenisch, R. & Young, R. Stem cells, the molecular circuitry of pluripotency and nuclear reprogramming. Cell 132, 567–582 (2008).

  49. 49.

    Yaeger, R. et al. Genomic alterations observed in colitis-associated cancers are distinct from those found in sporadic colorectal cancers and vary by type of inflammatory bowel disease. Gastroenterology 151, 278–287 e6 (2016).

  50. 50.

    Sharpless, N. E. & DePinho, R. A. The INK4A/ARF locus and its two gene products. Curr. Opin. Genet. Dev. 9, 22–30 (1999).

  51. 51.

    Chin, L. et al. Cooperative effects of INK4a and ras in melanoma susceptibility in vivo. Genes Dev. 11, 2822–2834 (1997).

  52. 52.

    Kamijo, T. et al. Functional and physical interactions of the ARF tumor suppressor with p53 and Mdm2. Proc. Natl Acad. Sci. USA 95, 8292–8297 (1998).

  53. 53.

    Pomerantz, J. et al. The Ink4a tumor suppressor gene product, p19Arf, interacts with MDM2 and neutralizes MDM2's inhibition of p53. Cell 92, 713–723 (1998).

  54. 54.

    Stott, F. J. et al. The alternative product from the human CDKN2A locus, p14(ARF), participates in a regulatory feedback loop with p53 and MDM2. EMBO J. 17, 5001–5014 (1998).

  55. 55.

    Zhang, Y., Xiong, Y. & Yarbrough, W. G. ARF promotes MDM2 degradation and stabilizes p53: ARF-INK4a locus deletion impairs both the Rb and p53 tumor suppression pathways. Cell 92, 725–734 (1998).

  56. 56.

    Lukas, J. et al. Retinoblastoma-protein-dependent cell-cycle inhibition by the tumour suppressor p16. Nature 375, 503–506 (1995).

  57. 57.

    Otterson, G. A., Kratzke, R. A., Coxon, A., Kim, Y. W. & Kaye, F. J. Absence of p16INK4 protein is restricted to the subset of lung cancer lines that retains wildtype RB. Oncogene 9, 3375–3378 (1994).

  58. 58.

    Russo, A. A., Tong, L., Lee, J. O., Jeffrey, P. D. & Pavletich, N. P. Structural basis for inhibition of the cyclin-dependent kinase Cdk6 by the tumour suppressor p16INK4a. Nature 395, 237–243 (1998).

  59. 59.

    Shapiro, G. I. et al. Reciprocal Rb inactivation and p16INK4 expression in primary lung cancers and cell lines. Cancer Res. 55, 505–509 (1995).

  60. 60.

    Tanaka, H. et al. Disruption of the RB pathway and cell-proliferative activity in non-small-cell lung cancers. Int. J. Cancer 79, 111–115 (1998).

  61. 61.

    Maley, C. C. et al. The combination of genetic instability and clonal expansion predicts progression to esophageal adenocarcinoma. Cancer Res. 64, 7629–7633 (2004).

  62. 62.

    Maley, C. C. et al. Selectively advantageous mutations and hitchhikers in neoplasms: p16 lesions are selected in Barrett's esophagus. Cancer Res. 64, 3414–3427 (2004).

  63. 63.

    Buas, M. F. et al. Integrative post-genome-wide association analysis of CDKN2A and TP53 SNPs and risk of esophageal adenocarcinoma. Carcinogenesis 35, 2740–2747 (2014).

  64. 64.

    Ku, S. Y. et al. Rb1 and Trp53 cooperate to suppress prostate cancer lineage plasticity, metastasis, and antiandrogen resistance. Science 355, 78–83 (2017).

  65. 65.

    Mu, P. et al. SOX2 promotes lineage plasticity and antiandrogen resistance in TP53- and RB1-deficient prostate cancer. Science 355, 84–88 (2017).

  66. 66.

    Shapiro, G. I., Edwards, C. D., Ewen, M. E. & Rollins, B. J. p16INK4A participates in a G1 arrest checkpoint in response to DNA damage. Mol. Cell Biol. 18, 378–387 (1998).

  67. 67.

    Bartkova, J. et al. DNA damage response as a candidate anti-cancer barrier in early human tumorigenesis. Nature 434, 864–870 (2005).

  68. 68.

    Gorgoulis, V. G. et al. Activation of the DNA damage checkpoint and genomic instability in human precancerous lesions. Nature 434, 907–913 (2005).

  69. 69.

    Chen, Z. et al. Selective Chk1 inhibitors differentially sensitize p53-deficient cancer cells to cancer therapeutics. Int. J. Cancer 119, 2784–2794 (2006).

  70. 70.

    Reaper, P. M. et al. Selective killing of ATM- or p53-deficient cancer cells through inhibition of ATR. Nat. Chem. Biol. 7, 428–430 (2011).

  71. 71.

    Gadhikar, M. A. et al. CDKN2A/p16 deletion in head and neck cancer cells is associated with CDK2 activation, replication stress, and vulnerability to CHK1 inhibition. Cancer Res. 78, 781–797 (2018).

  72. 72.

    Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

  73. 73.

    Lv, L. et al. Tetraploid cells from cytokinesis failure induce aneuploidy and spontaneous transformation of mouse ovarian surface epithelial cells. Cell Cycle 11, 2864–2875 (2012).

  74. 74.

    Shi, G. et al. Loss of the acinar-restricted transcription factor Mist1 accelerates Kras-induced pancreatic intraepithelial neoplasia. Gastroenterology 136, 1368–1378 (2009).

  75. 75.

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

  76. 76.

    Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

  77. 77.

    Wong, G. S. et al. Targeting wild-type KRAS-amplified gastroesophageal cancer through combined MEK and SHP2 inhibition. Nat. Med. 24, 968–977 (2018).

  78. 78.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  79. 79.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  80. 80.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

  81. 81.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  82. 82.

    Kim, J. et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat. Genet. 48, 600–606 (2016).

  83. 83.

    Boeva, V. et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 28, 423–425 (2012).

  84. 84.

    Scheinin, I. et al. DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly. Genome Res. 24, 2022–2032 (2014).

  85. 85.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

  86. 86.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  87. 87.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  88. 88.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

  89. 89.

    Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. & Golani, I. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125, 279–284 (2001).

  90. 90.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  91. 91.

    Sergushichev, A. A. et al. GAM: a web-service for integrated transcriptional and metabolic network analysis. Nucleic Acids Res. 44, W194–W200 (2016).

  92. 92.

    Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

  93. 93.

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

  94. 94.

    Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).

  95. 95.

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

  96. 96.

    Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 5, 1210–1223 (2015).

  97. 97.

    Cowley, G. S. et al. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci. Data 1, 140035 (2014).

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Acknowledgements

We thank A. Sperling, M. Korpal, A. Rustgi and S. Ganesan for insightful discussions and review of the manuscript; H. Singh, D. Micalizzi, D. Liu and A. Nagaraja for insightful discussions; S. Wang and the BWH CytoGenomics Core for assistance with karyotype analysis; A. Gad and L.-H. Ang for assistance with immunohistochemistry and immunofluorescence assays; the Dana-Farber/Harvard Cancer Center in Boston, MA, for the use of the Specialized Histopathology Core, which provided histology and immunohistochemistry services; Harvard Digestive Disease Center and NIH grant P30DK034854 for core services, resources, technology and expertise; the Center for Cancer Genome Discovery for their assistance and expertise in WES of murine tissue; the Dana-Farber/Harvard Cancer Center is supported in part by NCI Cancer Center Support Grant no. NIH 5 P30 CA06516. This work was funded by grants from the National Cancer Institute (P01 CA098101 and U54 CA1630004) to A.J.B. and the American Cancer Society Postdoctoral Fellowship, a KL2/CMERIT Harvard Catalyst Award, the Perry S. Levy Fund for Gastrointestinal Cancer Research, and NIH K08-DK120930 to N.S.S.

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Contributions

M.D.S. and J.M.M. contributed equally to the manuscript as joint third authors. N.S.S. and A.J.B conceived and designed the study. N.S.S. and A.J.B. developed the methodology. N.S.S., O.K. and Y.Z. performed the mouse experiments. M.D.S., R. Bronson, D.P. and E.S. reviewed and analyzed histopathology. N.S.S., J.M.M., C.B. and F.S.-V. performed transcriptomic and genomic analyses. R.F.-L., K.L.L. and R. Beroukhim performed and supervised pharmacogenomic analyses. N.S.S., O.K., G.D. and J.-B. Lazaro performed and analyzed DNA damage response and other experiments. J.-B. Liu contributed to resources. N.S.S., O.K., G.D. and A.J.B. performed the formal analysis. N.S.S. and A.J.B. wrote the original draft. N.S.S., O.K., G.D., M.D.S., J.M.M. and A.J.B. edited the manuscipt. N.S.S. and A.J.B. supervised the study. N.S.S. and A.J.B acquired funding for the study.

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Correspondence to Nilay S. Sethi or Adam J. Bass.

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Competing interests

N.S.S. is a consultant for HVH Precision Analytics. A.J.B. receives funding from Merck, Bayer and Novartis, and is an advisor to Earli and Helix Nano and a co-founder of Signet Therapeutics.

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Extended data

Extended Data Fig. 1 Dietary carcinogen exposure promotes premalignant and malignant gastric lesions.

a Schematic of the central hypothesis: Chronic inflammation and carcinogen exposure collaborate with early genomic alterations (for example TP53 mutations) to enable the development of premalignant gastric lesions and eventual invasive cancer. b, Schematic of p53WT mice treated with DCA, MNU, or DCA+MNU combination for 18 months. c, Dissection microscope images of lower esophagus and stomach flayed open along greater curvature (top panel) and histopathological H&E staining (bottom panel) of gastric antrum. *A subset of mice died before the endpoint of the experiment due to other carcinogen-induced cancers (e.g. thymomas). Scale bar = 125μM. d, Mutation signature analysis shows C→T changes characteristic of alkylating agent associated Signature 11. center line: median, lower hinge: the first quartile (Q1), upper hinge: the third quartile (Q3), extreme of the lower whisker: Q1 – 1.58 * (Q3 – Q1)/sqrt(n), extreme of the upper whisker: Q3 + 1.58 * (Q3 – Q1)/sqrt(n), n = 5. e, Mutation burden and copy number analysis of gastric lesions (n=5).

Extended Data Fig. 2 Lgr5-p53KO cells have a selective advantage in the setting of dietary carcinogen to promote premalignant gastric lesions.

a, Recombination specific PCR of DNA extract from gastric lesions of Lgr5-p53WT and Lgr5-p53KO mice. Data presented as mean ± s.d of three technical replicates. b, Schematic showing Lgr5-p53LSL-R270H experimental design; DCA/MNU treatment and tamoxifen injection schedule during indicated duration depicted below. c, Kaplan-Meier survival curve of Lgr5-p53R270H experiments; table shows frequency of dysplasia in DCA/MNU treated mice of indicated genotype. d, Schematic showing Lgr5-p53KO experimental design; DCA alone, MNU alone, or DCA/MNU combination as well as tamoxifen injection schedule during indicated duration depicted below. Table shows frequency of dysplasia in Lgr5-p53KO mice with indicated treatment after 12 months. *1/6 Cre-negative, tamoxifen-induced control mice treated with DCA/MNU developed dysplasia.

Extended Data Fig. 3 TP53-deleted human premalignant cells have a selective growth advantage in the setting of MNU.

a, Relative proliferation of CP-A cells treated with indicated concentrations of DCA; phase contrast images of CP-A cells treated with DMSO or 100μM DCA. b, Immunoblot showing protein levels of p53 in CP-A cells treated with DMSO or 50μM DCA. This experiment was repeated once with similar results. c, DNA content flow cytometry of CP-A cells expressing control orTP53 shRNA#2 CP-A cells treated with indicated MNU concentrations. This experiment was repeated once with similar results. d, Relative proliferation of CP-A cells treated with DMSO or indicated concentrations of MNU by Celltiter Glo. Data presented as mean ± s.d of three culture replicates; p-value calculated by two-sided Student’s t-test. e, Immunoblot showing protein levels of p53 in genetically modified CP-A cells; top panel shows protein from CP-A cells expressing control sgRNA/Cas9 or three p53 targeting sgRNAs/Cas9 after treatment with MNU 200ng/mL; bottom panel shows protein from CP-A cells expressing a scrambled or two targeting p53 shRNAs treated with DMSO, Doxorubicin, or Nutlin in the presence or absence of doxycycline. This experiment was repeated once. f, Relative proliferation of CP-A cells expressing control sgRNA/Cas9 or three p53 targeting sgRNA/Cas9 (top panel); expressing vector control, scramble control, or two p53 targeting shRNAs. Data presented as mean ± s.d of three culture replicates. g, Quantification of dsDNA breaks in CP-A cells expressing control sgRNA/Cas9 or three p53 targeting sgRNA/Cas9 in the setting of indicated concentrations of MNU by γH2AX immunofluorescence. Data presented as mean ± s.d of four technical and two cell culture replicates; p-value calculated by two-sided Student’s t-test of cell culture replicates. h, FPKM gene counts of TP53 in CP-A cells stably expressing two p53 shRNAs. i, Ratio of TP53 pathway target gene expression levels in CP-A cells expressing indicated p53 shRNA relative to scramble control (cell culture replicates shown). Source data

Extended Data Fig. 4 p53R270H/+ organoids have a selective growth advantage in the setting of dietary carcinogen exposure.

a, Immunoblot showing expression of mutant p53R270H in whole cell lysates derived from p53LSL-R270H/+ and p53LSL-R270H/R270H gastric organoids with or without AdenoCre induction; immunofluorescent images of p53LSL-R270H/+;mTmG gastric organoids before and after AdenoCre induction. Organoids without induction remain red indicating that recombination has not occurred, whereas those with AdenoCre induction convert to green indicating that recombination has occurred. This experiment was repeated once with similar results. b, Flow cytometry of eGFP+ Mist1-p53R270H/+ and td+ Mist1-p53+/- organoids cultured in DMSO or 50ng/mL MNU. Quantification of eGFP+ Mist1-p53R270H/+ and td+ Mist1-p53+/- organoids at passage 1 (P1) and 3 (P3). c, Phase contrast and immunofluorescent images of eGFP+ Mist1-p53R270H/+ and td+ Mist1-p53+/- organoids cultured in DMSO or 50ng/mL MNU. This experiment was repeated twice with similar results. d, Schematic depicting competitive growth advantage of p53-altered gastric organoids in the setting of MNU. Source data

Extended Data Fig. 5 Dysplastic Lgr5-p53KO gastric organoids capture properties of premalignant lesions.

a, Recombination specific PCR of DNA extract from premalignant dys-Lgr5-p53WT and dys-Lgr5-p53KO gastric organoids. Data presented as mean ± s.d of n = 3 technical replicates per group. b, Quantification of proliferation by CellTiter-Glo and phase contrast images of nondysplastic and dysplastic Lgr5-p53WT and Lgr5-p53KO gastric organoids in the presence or absence of Nutlin-3 (30 uM for 72 hours). Data presented as mean ± s.d. of n = 6 cell culture replicates. c, Representative images of phase contrast and GFP-immunofluorescent images from dys-Lgr5-p53KO and dys-Lgr5-p53WT gastric organoid cultures. The experiment was repeated once with similar results. d, Karyotype analysis of premalignant Lgr5-p53KO (n = 2 mice), Lgr5-p53WT (n = 2 mice), and Lgr5-p53WT + AdenoCre gastric organoids; chromosome count of 10 cells per group. Data presented as mean ± s.d.; p-values calculated by Ordinary One-way ANOVA with Sidak’s multiple comparisons adjustment. e, Copy number analysis of a patient specimen of a high-grade dysplastic Barrett’s metaplasia harboring TP53R175H mutation and genome doubling. f, Histopathology of primary tumor xenograft of Lgr5-p53KO gastric organoid showing features of dysplasia. This experiment was repeated twice with similar results. g, DAPI staining and GFP (Lgr5+) immunofluorescence of primary xenograft from Lgr5-p53KO gastric organoids. This experiment was repeated twice with similar results. h, Schematic of low-pass whole genome sequencing (LP-WGS) experiment of cultured and xenograft dys-Lgr5-p53KO gastric organoids. Common and distinct broad somatic copy number alterations that were only found in xenograft tissue are displayed.

Extended Data Fig. 6 Interferon signaling is upregulated in dysplastic Lgr5-p53KO gastric organoids.

a, mRNA expression of Trp53 in p53KO, p53WT, Lgr5-p53KO, Lgr5-p53WT, dys-Lgr5-p53KO and dys-Lgr5-p53WT gastric organoids. Data presented as mean ± s.d. of n = 2 (nondysplastic) and n = 3 (dysplastic) cell culture replicates. b, mRNA expression levels of Csf3, Cxcl10, and Ccl5 in p53KO, p53WT, Lgr5-p53KO, Lgr5-p53WT, dys-Lgr5-p53KO and dys-Lgr5-p53WT gastric organoids by RT-PCR. Data presented as mean ± s.d. of n = 3 cell culture replicates. c, Volcano plot of differential expressed gene-sets in p53KO gastric organoids plotted as normalized enrichment scores (NES) by -log10(p-value), where p-values for gene set enrichment scores were determined by randomly permuting genes (n=100,000 permutations). Individual inflammation gene sets are described with corresponding NES. d, Scatter plot of single sample GSEA (ssGSEA) of interferon pathway and Ccl5 mRNA expression in gastric cancer cell lines annotated by TP53 mutation status (n = 26 for TP53 mutant and n = 10 for TP53 wildtype). Gray shaded region represents 95% confidence interval of the linear regression fit. Pearson’s correlation value R = 0.6 and p-value computed based on a two-sided t-distribution. e, Schematic of proposed vicious feedback cycle in gastric premalignancy: chronic inflammation and carcinogen exposure selects for p53 mutant gastric cells, which in turn stimulate inflammatory cytokines, particularly Csf3, Cxcl10, Ccl5.

Extended Data Fig. 7 WNT pathway is upregulated in dysplastic Lgr5-p53KO gastric organoids.

a, WNT-reporter activity in adherent culture of dys-Lgr5-p53KO and dys-Lgr5-p53WT gastric cells following transient transfection. Data presented as mean ± s.d. of n = 4 cell culture replicates; p-value calculated by two-sided Mann Whitney test. b, Phase contrast images of dys-Lgr5-p53KO and dys-Lgr5-p53WT gastric organoids two days following the first and second passage in WNT independent media (DMEM + 10%FBS). This experiment was repeated twice with similar results. c, Gene network of significantly mutated genes in dysplastic gastric lesions from DCA/MNU-treated Lgr5-p53WT mice (n = 8) using GeNets (adjusted p-value = 0.00065 using Bonferroni correction) (Li et al., 2018).

Extended Data Fig. 8 CDKN2A is upregulated in p53KO premalignant lesions and co-altered with TP53 in human gastric cancer.

a, Scatter plot of single sample GSEA (ssGSEA) of p53 pathway and CDKN2A mRNA expression in gastric cancer cell lines (n = 36). Gray shaded region represents 95% confidence interval of the linear regression fit. Pearson’s correlation value R = −0.36 and p-value computed based on a two-sided t-distribution. b, Heatmap showing mRNA expression of CDKN2B, CDKN2C, CDKN2D, CDKN1B, CDKN1C in CP-A cell line genetically engineered to express two p53 targeting shRNA under doxycycline inducible conditions. c, Oncoporint from cbioportal showing alterations and mRNA expression in CDKN2A and TP53 in human gastric adenocarcinomas from TCGA (n = 478). Deep deletions (solid blue); amplifications (solid red); missense mutations in the COSMIC repository (solid green); nonsense or frameshift mutations (black); elevated mRNA expression > 2.0 (hollow red). Table showing number of cases with double, single, or no alteration in CDKN2A and TP53 in human gastric adenocarcinomas from TCGA (right). d, Oncoprint from cbioportal showing alterations in CDKN2A and TP53 in human esophageal adenocarcinomas from TCGA and (Dulak et al., 2013) combined data-set (n = 337). Deep deletions (solid blue); amplifications (solid red); missense mutations in the COSMIC repository (solid green); nonsense or frameshift mutations (black). Table showing number of cases with double, single, or no alteration in CDKN2A and TP53 in combined esophageal adenocarcinoma data set. e, Table showing number of cases with double, single, or no alteration in CDKN2A and TP53 in combined gastric and esophageal adenocarcinomas from patients treated at DFCI (n = 1299). Q-value determined by one-sided Fischer Exact Test with Benjamin-Hochberg FDR correction.

Extended Data Fig. 9 Co-deletion of CDKN2A and TP53 sensitizes to DNA damage response pathway blockade.

a, Dose-response curves of CHK1 inhibitor AZD7762 of two gastric cancer cell lines wildtype for either TP53 or CDKN2A and two gastric cancer cell lines with co-alteration of TP53 and CDKN2A. b, Relative dependency of 23 gastric cancer cell lines to CHEK1, CHEK2, and WEE1 shRNA-mediated knockdown as shown by CERES dependency score. Black rectangles indicate gastric cancer cell lines with simultaneous disruption of TP53 and CDKN2A. c, Dose-response curve of KE39 (TP53MUT, CDKN2AWT) and GSU (TP53MUT, CDKN2AMUT) gastric cancer cell lines to Prexasertib. Data presented as mean ± s.d. of n = 6 cell culture replicates at each dose; p-value calculated by Comparison of Fits based on differences in IC50. d, Dose-response curve of KE39 (TP53MUT, CDKN2AWT) and GSU (TP53MUT, CDKN2AMUT) gastric cancer cell lines to WEE1 inhibitor AZD1775. Data presented as mean ± s.d. of n = 6 cell culture replicates at each dose; p-value calculated by Comparison of Fits based on differences in IC50. e, Quantification by manual counting of number of organoids in experiment from Fig. 7f, g. f, Quantification of organoid size and number from dys-Lgr5-p53KO-control and dys-Lgr5-p53WT-p16KO gastric organoids treated for 36 hours with DMSO or indicated concentration of ATR inhibitor AZD6738 or WEE1 inhibitor AZD1775. Data presented as mean ± s.d. of n = 6–8 technical replicates at each dose. g, Representative phase contrast images from experiment (f).

Supplementary information

Supplementary Information

Supplementary Notes 1–8 and Tables 2 and 3

Reporting Summary

Supplementary Table 1

A list of significantly mutated genes determined by WES of the indicated gastric lesions.

Source data

Source Data Fig. 2

Unprocessed western blots.

Source Data Fig. 5

Unprocessed western blots.

Source Data Fig. 7

Unprocessed western blots.

Source Data Extended Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 4

Unprocessed western blots.

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Sethi, N.S., Kikuchi, O., Duronio, G.N. et al. Early TP53 alterations engage environmental exposures to promote gastric premalignancy in an integrative mouse model. Nat Genet 52, 219–230 (2020). https://doi.org/10.1038/s41588-019-0574-9

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