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

Nature Reviews Cancer volume 16, pages 251–265 (2016)Cite this article

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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.

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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.

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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.

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Author notes

  1. Giovanni Stracquadanio and Xuting Wang: These authors contributed equally to this work.

Authors and Affiliations

  1. Nuffield Department of Clinical Medicine, Ludwig Institute for Cancer Research, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK

    Giovanni Stracquadanio, Marsha D. Wallace, Anna M. Grawenda, Ping Zhang, Juliet Hewitt, Colin R. Goding, Benjamin Schuster-Böckler & Gareth L. Bond

  2. Environmental Genomics Group, Genome Integrity and Structural Biology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, 27709, North Carolina, USA

    Xuting Wang & Douglas A. Bell

  3. Oncology Department, Vall d'Hebron University Hospital, Passeig de la Vall D'Hebron 119, Barcelona, 08035, Spain

    Jorge Zeron-Medina

  4. Molecular and Population Genetics Laboratory, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK

    Francesc Castro-Giner & Ian P. Tomlinson

  5. Center for Computational Molecular Biology, Brown University, 115 Waterman Street, Providence, 02912, Rhode Island, USA

    Kamil J. Cygan & William G. Fairbrother

  6. Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, 70 Ship Street, Providence, 02903, Rhode Island, USA

    Kamil J. Cygan & William G. Fairbrother

  7. Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway

    Laurent F. Thomas & Pål Sætrom

  8. Department of Computer and Information Science, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway

    Pål Sætrom

  9. Department of Biology, Genetics, University of Pisa, Via Derna 1, Pisa, 56126, Italy

    Federica Gemignani & Stefano Landi

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  1. Giovanni Stracquadanio
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  2. Xuting Wang
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  18. Douglas A. Bell
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Correspondence to Douglas A. Bell or Gareth L. Bond.

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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.

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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

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