Dataset Information

Common genetic variants in the TP53 pathway and their impact on cancer.

ABSTRACT: The TP53 gene is well known to be the most frequently mutated gene in human cancer. In addition to mutations, there are >?20 different coding region single-nucleotide polymorphisms (SNPs) in the TP53 gene, as well as SNPs in MDM2, the negative regulator of p53. Several of these SNPs are known to alter p53 pathway function. This makes p53 rather unique among cancer-critical genes, e.g. the coding regions of other cancer-critical genes like Ha-Ras, RB, and PI3KCA do not have non-synonymous coding region SNPs that alter their function in cancer. The next frontier in p53 biology will consist of probing which of these coding region SNPs are moderately or strongly pathogenic and whether they influence cancer risk and the efficacy of cancer therapy. The challenge after that will consist of determining whether we can tailor chemotherapy to correct the defects for each of these variants. Here we review the SNPs in TP53 and MDM2 that show the most significant impact on cancer and other diseases. We also propose avenues for how this information can be used to better inform personalized medicine approaches to cancer and other diseases.

SUBMITTER: Barnoud T

PROVIDER: S-EPMC6736421 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Common genetic variants in the TP53 pathway and their impact on cancer.

Barnoud Thibaut T Parris Joshua L D JLD Murphy Maureen E ME

Journal of molecular cell biology 20190701 7

The TP53 gene is well known to be the most frequently mutated gene in human cancer. In addition to mutations, there are > 20 different coding region single-nucleotide polymorphisms (SNPs) in the TP53 gene, as well as SNPs in MDM2, the negative regulator of p53. Several of these SNPs are known to alter p53 pathway function. This makes p53 rather unique among cancer-critical genes, e.g. the coding regions of other cancer-critical genes like Ha-Ras, RB, and PI3KCA do not have non-synonymous coding ...[more]

PMID: 31152665

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Project description:Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. A study was conducted in a large multi-population study to assess the feasibility of CRC risk prediction using common genetic variant data combined with other risk factors. A risk prediction model was built and applied to the Scottish population using available data.Nine populations of European descent were studied to develop and validate CRC risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39,266) and in combination with gender, age and FH (n=11,324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks.The median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2 × 10(-16)), confirmed in external validation sets (Sweden p=1.2 × 10(-6), Finland p=2 × 10(-5)). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve for genotype/age/gender/FH 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk.Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.

Dataset Information

Common genetic variants in the TP53 pathway and their impact on cancer.

Publications

Common genetic variants in the TP53 pathway and their impact on cancer.

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