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.

The importance of p53 pathway genetics in inherited and somatic cancer genomes

Key Points

  • In this Analysis, we explore the possibility that commonly inherited genetic variants in the p53 pathway play a significant part in susceptibility to a broad range of cancers.

  • We use genome-wide datasets of genetic variation, cancer susceptibility loci derived from hundreds of genome-wide association studies conducted in a broad range of cancers, and expression quantitative trait loci (eQTLs) from eQTL databases from many different tissue types.

  • Our results demonstrate that p53 pathway genes are more significantly enriched in cancer susceptibility loci compared with other signalling pathways.

  • We did not find p53 pathway genes to be significantly enriched in susceptibility loci for any other major disease groupings.

  • We observe strong similarities between the causal, somatic mutations and the inherited, cancer-associated single nucleotide polymorphisms of the p53 pathway, in which both classes of genetic variant are found to occur in a high proportion of p53 pathway genes in multiple cancer types, and in similar genes.

  • Our results enable insights into p53-mediated tumour suppression in humans and into p53 pathway-based cancer surveillance and treatment strategies.

Abstract

Decades of research have shown that mutations in the p53 stress response pathway affect the incidence of diverse cancers more than mutations in other pathways. However, most evidence is limited to somatic mutations and rare inherited mutations. Using newly abundant genomic data, we demonstrate that commonly inherited genetic variants in the p53 pathway also affect the incidence of a broad range of cancers more than variants in other pathways. The cancer-associated single nucleotide polymorphisms (SNPs) of the p53 pathway have strikingly similar genetic characteristics to well-studied p53 pathway cancer-causing somatic mutations. Our results enable insights into p53-mediated tumour suppression in humans and into p53 pathway-based cancer surveillance and treatment strategies.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Somatic, causal mutations occur in a high proportion of p53 pathway genes.
Figure 2: One hundred and sixty-five genome-wide association studies (GWAS) of many types of cancer have been carried out in European populations.
Figure 3: Cancer-associated single nucleotide polymorphisms (SNPs) occur in a high proportion of p53 pathway genes.
Figure 4: Cancer-associated expression quantitative trait loci (eQTLs) occur in a high proportion of p53 pathway genes.
Figure 5: p53 pathway genes are significantly enriched in cancer susceptibility genes, but not susceptibility genes for other major disease groupings.
Figure 6: Cancer susceptibility gene enrichment in p53 pathway genes is not limited to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation.
Figure 7: Both p53 pathway mutations and cancer-associated single nucleotide polymorphisms (SNPs) occur in a high proportion of pathway genes in multiple cancer types.
Figure 8: p53 pathway cancer susceptibility genes (CSGs) are causally mutated in cancer.

References

  1. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013). This paper takes advantage of the ever-increasing knowledge of cancer genome sequences to define and classify cancer driver gene mutations and begins to place them into signalling pathways.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Edwards, S. L., Beesley, J., French, J. D. & Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Manolio, T. A. Bringing genome-wide association findings into clinical use. Nat. Rev. Genet. 14, 549–558 (2013). This paper summarizes the impact of GWAS on the clinic and laboratory and discusses their future impact.

    CAS  Article  PubMed  Google Scholar 

  4. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  5. Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    CAS  Article  Google Scholar 

  6. Schodel, J. et al. Common genetic variants at the 11q13.3 renal cancer susceptibility locus influence binding of HIF to an enhancer of cyclin D1 expression. Nat. Genet. 44, 420–425 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. Sur, I. K. et al. Mice lacking a Myc enhancer that includes human SNP rs6983267 are resistant to intestinal tumors. Science 338, 1360–1363 (2012). This paper describes a polymorphic transcriptional regulatory element in MYC that is able to affect cancer susceptibility in a mouse model of intestinal cancer.

    CAS  PubMed  Article  Google Scholar 

  8. Zeron-Medina, J. et al. A polymorphic p53 response element in KIT ligand influences cancer risk and has undergone natural selection. Cell 155, 410–422 (2013). This paper describes a SNP in a functional p53 response element that has undergone positive selection and influences testicular cancer risk.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Cookson, W., Liang, L., Abecasis, G., Moffatt, M. & Lathrop, M. Mapping complex disease traits with global gene expression. Nat. Rev. Genet. 10, 184–194 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Stranger, B. E. et al. Patterns of cis regulatory variation in diverse human populations. PLoS Genet. 8, e1002639 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Veyrieras, J. B. et al. High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet. 4, e1000214 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. Nica, A. C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  14. McBride, K. A. et al. Li-Fraumeni syndrome: cancer risk assessment and clinical management. Nat. Rev. Clin. Oncol. 11, 260–271 (2014). This paper reviews the effect of TP53 germline mutations on a heritable cancer syndrome and the clinical implications of heritable TP53 mutations for cancer diagnosis and prevention.

    CAS  Article  PubMed  Google Scholar 

  15. Merino, D. & Malkin, D. p53 and hereditary cancer. Subcell. Biochem. 85, 1–16 (2014).

    Article  PubMed  Google Scholar 

  16. Vazquez, A., Bond, E. E., Levine, A. J. & Bond, G. L. The genetics of the p53 pathway, apoptosis and cancer therapy. Nat. Rev. Drug Discov. 7, 979–987 (2008).

    CAS  PubMed  Article  Google Scholar 

  17. Grochola, L. F., Zeron-Medina, J., Meriaux, S. & Bond, G. L. Single-nucleotide polymorphisms in the p53 signaling pathway. Cold Spring Harb. Perspect. Biol. 2, a001032 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. Whibley, C. & Pharoah, P. D. & Hollstein, M. p53 polymorphisms: cancer implications. Nat. Rev. Cancer 9, 95–107 (2009).

    CAS  PubMed  Article  Google Scholar 

  19. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013). This paper analyses cancer genome sequencing data to describe the distributions of somatic mutations across tumour types, wherein the TP53 gene is found to be the most frequently mutated gene.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Soussi, T., Ishioka, C., Claustres, M. & Beroud, C. Locus-specific mutation databases: pitfalls and good practice based on the p53 experience. Nat. Rev. Cancer 6, 83–90 (2006).

    CAS  PubMed  Article  Google Scholar 

  21. Leroy, B. et al. The TP53 website: an integrative resource centre for the TP53 mutation database and TP53 mutant analysis. Nucleic Acids Res. 41, D962–D969 (2013).

    CAS  PubMed  Article  Google Scholar 

  22. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  23. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  24. The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

  25. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

  26. Kandoth, C. et al. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).

    PubMed  Article  CAS  Google Scholar 

  27. Leiserson, M. D. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015). This paper develops and uses a novel algorithm to define cancer driver genes and integrate them into interacting networks. The authors clearly note that TP53 belongs to the largest mutated subnetwork found in the broadest spectrum of cancers.

    CAS  PubMed  Article  Google Scholar 

  28. Khoo, K. H., Verma, C. S. & Lane, D. P. Drugging the p53 pathway: understanding the route to clinical efficacy. Nat. Rev. Drug Discov. 13, 217–236 (2014). This paper comprehensively describes the development of druggable targets in the p53 pathway and their clinical impact.

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Wade, M., Li, Y. C. & Wahl, G. M. MDM2, MDMX and p53 in oncogenesis and cancer therapy. Nat. Rev. Cancer 13, 83–96 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Ventura, A. et al. Restoration of p53 function leads to tumour regression in vivo. Nature 445, 661–665 (2007).

    CAS  PubMed  Article  Google Scholar 

  32. Xue, W. et al. Senescence and tumour clearance is triggered by p53 restoration in murine liver carcinomas. Nature 445, 656–660 (2007). References 31 and 32 demonstrate that restoration of p53 activity inhibits tumorigenesis in mice by activating apoptosis and/or senescence.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Riley, T., Sontag, E., Chen, P. & Levine, A. Transcriptional control of human p53-regulated genes. Nat. Rev. Mol. Cell Biol. 9, 402–412 (2008).

    CAS  Article  PubMed  Google Scholar 

  34. Muller, P. A., Vousden, K. H. & Norman, J. C. p53 and its mutants in tumor cell migration and invasion. J. Cell Biol. 192, 209–218 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Muller, P. A. & Vousden, K. H. p53 mutations in cancer. Nat. Cell Biol. 15, 2–8 (2013). This paper reviews the oncogenic properties and mechanisms of the gain-of-function p53 mutants.

    CAS  Article  PubMed  Google Scholar 

  36. Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Brown, C. D., Mangravite, L. M. & Engelhardt, B. E. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genet. 9, e1003649 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Fairfax, B. P. et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Fehrmann, R. S. et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 7, e1002197 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Gaffney, D. J. et al. Dissecting the regulatory architecture of gene expression QTLs. Genome Biol. 13, R7 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Hao, K. et al. Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet. 8, e1003029 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Innocenti, F. et al. Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS Genet. 7, e1002078 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Liang, L. et al. A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines. Genome Res. 23, 716–726 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Schadt, E. E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, e107 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. Zeller, T. et al. Genetics and beyond—the transcriptome of human monocytes and disease susceptibility. PLoS ONE 5, e10693 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. Bryois, J. et al. Cis and trans effects of human genomic variants on gene expression. PLoS Genet. 10, e1004461 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. Kabakchiev, B. & Silverberg, M. S. Expression quantitative trait loci analysis identifies associations between genotype and gene expression in human intestine. Gastroenterology 144, 1488–1496 (2013).

    CAS  PubMed  Article  Google Scholar 

  50. Koopmann, T. T. et al. Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart. PLoS ONE 9, e97380 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  51. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Alves da Costa, C. et al. Presenilin-dependent γ-secretase-mediated control of p53-associated cell death in Alzheimer's disease. J. Neurosci. 26, 6377–6385 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Fogarty, M. P. et al. A role for p53 in the β-amyloid-mediated regulation of the lysosomal system. Neurobiol. Aging 31, 1774–1786 (2010).

    CAS  PubMed  Article  Google Scholar 

  54. Perier, C. et al. Two molecular pathways initiate mitochondria-dependent dopaminergic neurodegeneration in experimental Parkinson's disease. Proc. Natl Acad. Sci. USA 104, 8161–8166 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. Alves da Costa, C. et al. Transcriptional repression of p53 by parkin and impairment by mutations associated with autosomal recessive juvenile Parkinson's disease. Nat. Cell Biol. 11, 1370–1375 (2009).

    CAS  Article  Google Scholar 

  56. Feng, Z. et al. p53 tumor suppressor protein regulates the levels of huntingtin gene expression. Oncogene 25, 1–7 (2006).

    PubMed  Article  CAS  Google Scholar 

  57. Matsumoto, S. et al. Circulating p53-responsive microRNAs are predictive indicators of heart failure after acute myocardial infarction. Circ. Res. 113, 322–326 (2013).

    CAS  PubMed  Article  Google Scholar 

  58. Sano, M. et al. p53-induced inhibition of Hif-1 causes cardiac dysfunction during pressure overload. Nature 446, 444–448 (2007).

    CAS  PubMed  Article  Google Scholar 

  59. Munoz-Fontela, C. et al. p53 serves as a host antiviral factor that enhances innate and adaptive immune responses to influenza A virus. J. Immunol. 187, 6428–6436 (2011).

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  61. Garcia, P. B. & Attardi, L. D. Illuminating p53 function in cancer with genetically engineered mouse models. Semin. Cell Dev. Biol. 27, 74–85 (2014).

    CAS  PubMed  Article  Google Scholar 

  62. Guarini, A. et al. ATM gene alterations in chronic lymphocytic leukemia patients induce a distinct gene expression profile and predict disease progression. Haematologica 97, 47–55 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Renwick, A. et al. ATM mutations that cause ataxia-telangiectasia are breast cancer susceptibility alleles. Nat. Genet. 38, 873–875 (2006).

    CAS  PubMed  Article  Google Scholar 

  64. Kim, H. S. et al. Inactivating mutations of caspase-8 gene in colorectal carcinomas. Gastroenterology 125, 708–715 (2003).

    CAS  PubMed  Article  Google Scholar 

  65. Soung, Y. H. et al. Caspase-8 gene is frequently inactivated by the frameshift somatic mutation 1225_1226delTG in hepatocellular carcinomas. Oncogene 24, 141–147 (2005).

    CAS  PubMed  Article  Google Scholar 

  66. Wiestner, A. et al. Point mutations and genomic deletions in CCND1 create stable truncated cyclin D1 mRNAs that are associated with increased proliferation rate and shorter survival. Blood 109, 4599–4606 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. Gao, Y. B. et al. Genetic landscape of esophageal squamous cell carcinoma. Nat. Genet. 46, 1097–1102 (2014).

    CAS  Article  PubMed  Google Scholar 

  68. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  69. Sieuwerts, A. M. et al. Which cyclin E prevails as prognostic marker for breast cancer? Results from a retrospective study involving 635 lymph node-negative breast cancer patients. Clin. Cancer Res. 12, 3319–3328 (2006).

    CAS  PubMed  Article  Google Scholar 

  70. Nakayama, N. et al. Gene amplification CCNE1 is related to poor survival and potential therapeutic target in ovarian cancer. Cancer 116, 2621–2634 (2010).

    CAS  PubMed  Article  Google Scholar 

  71. Gronbaek, K. et al. Concurrent disruption of p16INK4a and the ARF-p53 pathway predicts poor prognosis in aggressive non-Hodgkin's lymphoma. Leukemia 14, 1727–1735 (2000).

    CAS  PubMed  Article  Google Scholar 

  72. Holzelova, E. et al. Autoimmune lymphoproliferative syndrome with somatic Fas mutations. N. Engl. J. Med. 351, 1409–1418 (2004).

    CAS  PubMed  Article  Google Scholar 

  73. Dowdell, K. C. et al. Somatic FAS mutations are common in patients with genetically undefined autoimmune lymphoproliferative syndrome. Blood 115, 5164–5169 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. Park, W. S. et al. Somatic mutations in the death domain of the Fas (Apo-1/CD95) gene in gastric cancer. J. Pathol. 193, 162–168 (2001).

    CAS  PubMed  Article  Google Scholar 

  75. Gembarska, A. et al. MDM4 is a key therapeutic target in cutaneous melanoma. Nat. Med. 18, 1239–1247 (2012).

    CAS  PubMed  Article  Google Scholar 

  76. Forbes, S. A. et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945–D950 (2011).

    CAS  PubMed  Article  Google Scholar 

  77. Stacey, S. N. et al. A germline variant in the TP53 polyadenylation signal confers cancer susceptibility. Nat. Genet. 43, 1098–1103 (2011). This paper describes a SNP in the TP53 gene that affects the poly(A) signal sequence, thus affecting cancer risk.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. Wang, Z. et al. Further confirmation of germline glioma risk variant rs78378222 in TP53 and its implication in tumor tissues via integrative analysis of TCGA data. Hum. Mutat. 36, 684–688 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  79. Weinhold, N. et al. The CCND1 c.870G>A polymorphism is a risk factor for t(11;14)(q13;q32) multiple myeloma. Nat. Genet. 45, 522–525 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. Betticher, D. C. et al. Alternate splicing produces a novel cyclin D1 transcript. Oncogene 11, 1005–1011 (1995).

    CAS  PubMed  Google Scholar 

  81. Comstock, C. E. et al. Cyclin D1 splice variants: polymorphism, risk, and isoform-specific regulation in prostate cancer. Clin. Cancer Res. 15, 5338–5349 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. Olshavsky, N. A. et al. Identification of ASF/SF2 as a critical, allele-specific effector of the cyclin D1b oncogene. Cancer Res. 70, 3975–3984 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. Knudsen, K. E., Diehl, J. A., Haiman, C. A. & Knudsen, E. S. Cyclin D1: polymorphism, aberrant splicing and cancer risk. Oncogene 25, 1620–1628 (2006).

    CAS  Article  PubMed  Google Scholar 

  84. Kruse, J. P. & Gu, W. Modes of p53 regulation. Cell 137, 609–622 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. Bieging, K. T. & Attardi, L. D. Deconstructing p53 transcriptional networks in tumor suppression. Trends Cell Biol. 22, 97–106 (2012).

    CAS  Article  PubMed  Google Scholar 

  86. Fumagalli, S. & Thomas, G. The role of p53 in ribosomopathies. Semin. Hematol. 48, 97–105 (2011).

    CAS  PubMed  Article  Google Scholar 

  87. McGowan, K. A. & Mason, P. J. Animal models of Diamond Blackfan anemia. Semin. Hematol. 48, 106–116 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. Van Nostrand, J. L. & Attardi, L. D. Guilty as CHARGED: 53's expanding role in disease. Cell Cycle 13, 3798–3807 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. Van Nostrand, J. L. et al. Inappropriate p53 activation during development induces features of CHARGE syndrome. Nature 514, 228–232 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. Adachi, M. et al. Targeted mutation in the Fas gene causes hyperplasia in peripheral lymphoid organs and liver. Nat. Genet. 11, 294–300 (1995).

    CAS  PubMed  Article  Google Scholar 

  91. Adachi, M. et al. Enhanced and accelerated lymphoproliferation in Fas-null mice. Proc. Natl Acad. Sci. USA 93, 2131–2136 (1996).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  92. Barlow, C. et al. Atm-deficient mice: a paradigm of ataxia telangiectasia. Cell 86, 159–171 (1996).

    CAS  PubMed  Article  Google Scholar 

  93. Bieging, K. T., Mello, S. S. & Attardi, L. D. Unravelling mechanisms of p53-mediated tumour suppression. Nat. Rev. Cancer 14, 359–370 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. Chen, L. et al. CD95 promotes tumour growth. Nature 465, 492–496 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. Deane, N. G. et al. Hepatocellular carcinoma results from chronic cyclin D1 overexpression in transgenic mice. Cancer Res. 61, 5389–5395 (2001).

    CAS  PubMed  Google Scholar 

  96. Geng, Y. et al. Kinase-independent function of cyclin E. Mol. Cell 25, 127–139 (2007).

    CAS  PubMed  Article  Google Scholar 

  97. Geng, Y. et al. Cyclin E ablation in the mouse. Cell 114, 431–443 (2003).

    CAS  PubMed  Article  Google Scholar 

  98. Hakem, A. et al. Caspase-8 is essential for maintaining chromosomal stability and suppressing B-cell lymphomagenesis. Blood 119, 3495–3502 (2012).

    CAS  PubMed  Article  Google Scholar 

  99. Jiang, H. et al. The combined status of ATM and p53 link tumor development with therapeutic response. Genes Dev. 23, 1895–1909 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. Kamijo, T. et al. Tumor suppression at the mouse INK4a locus mediated by the alternative reading frame product p19ARF. Cell 91, 649–659 (1997).

    CAS  PubMed  Article  Google Scholar 

  101. Kwong, L. N., Weiss, K. R., Haigis, K. M. & Dove, W. F. Atm is a negative regulator of intestinal neoplasia. Oncogene 27, 1013–1018 (2008).

    CAS  PubMed  Article  Google Scholar 

  102. Liu, S. C. et al. Overexpression of cyclin D2 is associated with increased in vivo invasiveness of human squamous carcinoma cells. Mol. Carcinogen. 34, 131–139 (2002).

    Article  CAS  Google Scholar 

  103. Salmena, L. et al. Essential role for caspase 8 in T-cell homeostasis and T-cell-mediated immunity. Genes Dev. 17, 883–895 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. Varfolomeev, E. E. et al. Targeted disruption of the mouse caspase 8 gene ablates cell death induction by the TNF receptors, Fas/Apo1, and DR3 and is lethal prenatally. Immunity 9, 267–276 (1998).

    CAS  Article  PubMed  Google Scholar 

  105. Xiong, S. et al. Spontaneous tumorigenesis in mice overexpressing the p53-negative regulator Mdm4. Cancer Res. 70, 7148–7154 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  106. Post, S. M. et al. A high-frequency regulatory polymorphism in the p53 pathway accelerates tumor development. Cancer Cell 18, 220–230 (2010). This is the first description of a human regulatory SNP affecting tumorigenesis in a mouse model.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. Balint, E. E. & Vousden, K. H. Activation and activities of the p53 tumour suppressor protein. Br. J. Cancer 85, 1813–1823 (2001).

    CAS  PubMed Central  Article  Google Scholar 

  108. Feng, Z. et al. The regulation of AMPK β1, TSC2, and PTEN expression by p53: stress, cell and tissue specificity, and the role of these gene products in modulating the IGF-1–AKT–mTOR pathways. Cancer Res. 67, 3043–3053 (2007).

    CAS  PubMed  Article  Google Scholar 

  109. Harris, S. L. & Levine, A. J. The p53 pathway: positive and negative feedback loops. Oncogene 24, 2899–2908 (2005).

    CAS  PubMed  Article  Google Scholar 

  110. Hofseth, L. J., Hussain, S. P. & Harris, C. C. p53: 25 years after its discovery. Trends Pharmacol. Sci. 25, 177–181 (2004).

    CAS  PubMed  Article  Google Scholar 

  111. Levine, A. J., Hu, W. & Feng, Z. The p53 pathway: what questions remain to be explored? Cell Death Differ. 13, 1027–1036 (2006).

    CAS  PubMed  Article  Google Scholar 

  112. Sherr, C. J. Divorcing ARF and p53: an unsettled case. Nat. Reviews Cancer 6, 663–673 (2006).

    CAS  PubMed  Article  Google Scholar 

  113. Tokino, T. & Nakamura, Y. The role of p53-target genes in human cancer. Crit. Rev. Oncol. Hematol. 33, 1–6 (2000).

    CAS  PubMed  Article  Google Scholar 

  114. Donehower, L. A. & Lozano, G. 20 years studying p53 functions in genetically engineered mice. Nat. Rev. Cancer 9, 831–841 (2009).

    CAS  PubMed  Article  Google Scholar 

  115. Kamijo, T., Bodner, S., van de Kamp, E., Randle, D. H. & Sherr, C. J. Tumor spectrum in ARF-deficient mice. Cancer Res. 59, 2217–2222 (1999).

    CAS  PubMed  Google Scholar 

  116. Spring, K. et al. Mice heterozygous for mutation in Atm, the gene involved in ataxia-telangiectasia, have heightened susceptibility to cancer. Nat. Genet. 32, 185–190 (2002).

    CAS  PubMed  Article  Google Scholar 

  117. Spring, K. et al. Atm knock-in mice harboring an in-frame deletion corresponding to the human ATM 7636del9 common mutation exhibit a variant phenotype. Cancer Res. 61, 4561–4568 (2001).

    CAS  PubMed  Google Scholar 

  118. Wang, Y. V., Leblanc, M., Wade, M., Jochemsen, A. G. & Wahl, G. M. Increased radioresistance and accelerated B cell lymphomas in mice with Mdmx mutations that prevent modifications by DNA-damage-activated kinases. Cancer Cell 16, 33–43 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  119. Yamamoto, K. et al. Kinase-dead ATM protein causes genomic instability and early embryonic lethality in mice. J. Cell Biol. 198, 305–313 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

This work was funded in part by the Ludwig Institute for Cancer Research, the Nuffield Department of Medicine, the Development Fund, Oxford Cancer Research Centre, University of Oxford, UK, and by the Intramural Research Program of the National Institute of Environmental Health Sciences, US National Institutes of Health (projects: Z01ES100475 and Z01ES46008). We thank J. S. Bader, M. Muers, M. Resnick and B. A. Merrick for their critical reading of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Douglas A. Bell or Gareth L. Bond.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Supplementary information

Supplementary information S1 (methods) | Analytical procedures. (PDF 565 kb)

Supplementary information S2 (table) | Somatically mutated genes enrichment analysis for KEGG pathways. (PDF 36406 kb)

41568_2016_BFnrc201615_MOESM209_ESM.pdf

Supplementary information S3 (figure) | Somatic, causal mutations occur in a high proportion of p53 pathway genes. (PDF 173 kb)

Supplementary information S4 (table) | Diseases classification. (PDF 36406 kb)

Supplementary information S5 (table) | Autosomal Cancer Susceptibility Genes. (PDF 36406 kb)

Supplementary information S6 (table) | Cancer Susceptibility Gene (CSGs) Enrichment in KEGG pathways. (PDF 36406 kb)

41568_2016_BFnrc201615_MOESM211_ESM.pdf

Supplementary information S7 (table) | eQTL-Cancer Susceptibility Gene (eCSG) Enrichment in KEGG pathways. (PDF 36406 kb)

Supplementary information S8 (table) | Disease Susceptibility Gene (SGs) Enrichment in KEGG pathways. (PDF 36406 kb)

Supplementary information S9 (table) | Susceptibility Genes (SGs) Enrichment in BioCarta pathways. (PDF 36406 kb)

Supplementary information S10 (table) | Susceptibility Genes (SGs) Enrichment in Panther pathways. (PDF 36406 kb)

41568_2016_BFnrc201615_MOESM215_ESM.pdf

Supplementary information S11 (table) | Somatically mutated gene enrichment analysis for KEGG pathways across different cancer types. (PDF 36406 kb)

41568_2016_BFnrc201615_MOESM216_ESM.pdf

Supplementary information S12 (table) | CSGs enrichment analysis for KEGG pathways across different cancer types. (PDF 36406 kb)

41568_2016_BFnrc201615_MOESM217_ESM.pdf

Supplementary information S13 (table) | The 12 CSGs annotated to the p53 pathway by at least one database. (PDF 36406 kb)

41568_2016_BFnrc201615_MOESM218_ESM.pdf

Supplementary information S14 (table) | The 7 eCSGs annotated to the p53 pathway by at least one database. (PDF 36406 kb)

Supplementary information S15 (table) | Functional SNPs in p53 pathway genes. (PDF 36406 kb)

41568_2016_BFnrc201615_MOESM220_ESM.pdf

Supplementary information S16 (table) | p53 non-cancer SGs annotated to the p53 pathway by at least one database. (PDF 36406 kb)

Glossary

Genome-wide association studies

(GWAS). Analysis of the association of genetic variants, typically single nucleotide polymorphisms (SNPs), with a specific trait or disease. They are often very large case–control studies in which SNPs throughout the whole genome are examined for differences in allele frequencies between the two different populations.

Expression quantitative trait loci

(eQTLs). Genetic variants in the genome, typically single nucleotide polymorphisms (SNPs) or copy number variants, that are associated with differential expression of a gene. Typically, global gene expression measurements and whole-genome SNP genotypes are correlated to connect the abundance of a specific gene transcript with an allelic variant and define eQTLs.

Li–Fraumeni syndrome

(LFS). A familial cancer predisposition syndrome associated with certain cancers arising in multiple tissues, such as soft tissue sarcomas, breast cancer, leukaemia and osteosarcomas; 50% of patients are heterozygous for cancer-causing mutations in TP53. Increased cancer risk is extremely high and has been estimated to be 50% by the age of 40 years and 90% by the age of 60 years.

Multiple hypothesis testing

When testing many hypotheses simultaneously, the likelihood of one test reaching a significance threshold of P <0.05 increases. Thus, to reduce the likelihood of false positives, a multiple hypothesis testing correction is applied.

Linkage disequilibrium

(LD). The non-random association of alleles of two or more single nucleotide polymorphisms (SNPs). LD is influenced by many factors, including mutation rate, recombination, chromosomal distance, natural selection and genetic drift. It has been extensively used in the design and interpretation of genome-wide association studies (GWAS). A commonly used measure of linkage disequilibrium between two loci is the squared correlation or r2.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Stracquadanio, G., Wang, X., Wallace, M. et al. The importance of p53 pathway genetics in inherited and somatic cancer genomes. Nat Rev Cancer 16, 251–265 (2016). https://doi.org/10.1038/nrc.2016.15

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc.2016.15

Further reading

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