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TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes
Abstract
A large amount of data is available on the functional impact of missense mutations in TP53 and on mutation patterns in many different cancers. New data on mutant p53 protein function, cancer phenotype and prognosis have recently been integrated in the International Agency for Research on Cancer TP53 database (http://www-p53.iarc.fr/). Based on these data, we summarize here current knowledge on the respective roles of mutagenesis and biological selection of mutations with specific functional characteristic in shaping the patterns and phenotypes of mutations observed in human cancers. The main conclusion is that intrinsic mutagenicity rates, loss of transactivation activities, and to a lesser extent, dominant-negative activities are the main driving forces that determine TP53 mutation patterns and influence tumor phenotype. In contrast, current experimental data on the acquisition of oncogenic activities (gain of function) by p53 mutants are too scarce and heterogenous to assess whether this property has an impact on tumor development and outcome. In the case of inherited TP53 mutations causing Li-Fraumeni and related syndromes, the age at onset of some tumor types is in direct relation with the degree of loss of transactivation capacity of missense mutations. Finally, studies on large case series demonstrate that TP53 mutations are independent markers of bad prognosis in breast and several other cancers, and that the exact type and position of the mutation influences disease outcome. Further studies are needed to determine how TP53 haplotypes or loss of alleles interact with mutations to modulate their impact on cancer development and prognosis.
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“Although mut-p53 has been correlated to poorer prognosis in breast and other cancers [32], the mutational status of TP53 and its various pathway elements (ARF-MDM2/4) has not been correlated with survival in GBM, despite the high mutation frequency [27,32,33,34].”
From The p53 Pathway in Glioblastoma (2018) · cites this paper as [32]
In the citing paper
“MDM2 amplifications have not been shown to correlate with GBM survival and/or prognosis [27,32,33,34].”
From The p53 Pathway in Glioblastoma (2018) · cites this paper as [32]
In the citing paper
“CDKN2A/ARF loss has not been shown to correlate with GBM prognosis and/or survival [27,32,33,34].”
From The p53 Pathway in Glioblastoma (2018) · cites this paper as [32]
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Metadata
- Authors
- Petitjean, A., Achatz, M. I. W., Borresen-Dale, A. L., Hainaut, P., & Olivier, M.
- Affiliations
- Centre international de recherche sur le cancer (FR)
- Corresponding author
- Hainaut, P
- Venue
- Oncogene
- Published
- 2007-04-02
- Volume
- 26
- Issue
- 15
- Pages
- 2157-2165
- ISSN
- 0950-9232 (ISSN-L)
- 1476-5594
- OpenAlex
- W2042484101
- Cited by
- 884
- References
- 117
- Citations by year
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- FWCI
- 5.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Oncogene (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cancer-related Molecular Pathways
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Pathology and Forensic Medicine › Genetic factors in colorectal cancer
- Keywords
- TP53 mutation, missense mutations, dominant-negative p53, gain-of-function, mutagenesis, tumor phenotype, cancer prognosis, Li-Fraumeni syndrome, loss of heterozygosity, breast cancer
- MeSH terms
- Genetic Markers, Humans, Mutation, Neoplasms, Polymorphism, Genetic, Prognosis, Biomarkers, Tumor, Tumor Suppressor Protein p53, Mutagenesis, Li-Fraumeni Syndrome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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