Dataset Information

Somatic TP53 Mutations in the Era of Genome Sequencing.

ABSTRACT: Amid the complexity of genetic alterations in human cancer, TP53 mutation appears as an almost invariant component, representing by far the most frequent genetic alteration overall. Compared with previous targeted sequencing studies, recent integrated genomics studies offer a less biased view of TP53 mutation patterns, revealing that >20% of mutations occur outside the DNA-binding domain. Among the 12 mutations representing each at least 1% of all mutations, five occur at residues directly involved in specific DNA binding, four affect the tertiary fold of the DNA-binding domain, and three are nonsense mutations, two of them in the carboxyl terminus. Significant mutations also occur in introns, affecting alternative splicing events or generating rearrangements (e.g., in intron 1 in sporadic osteosarcoma). In aggressive cancers, mutation is so common that it may not have prognostic value (all these cancers have impaired p53 function caused by mutation or by other mechanisms). In several other cancers, however, mutation makes a clear difference for prognostication, as, for example, in HER2-enriched breast cancers and in lung adenocarcinoma with EGFR mutations. Thus, the clinical significance of TP53 mutation is dependent on tumor subtype and context. Understanding the clinical impact of mutation will require integrating mutation-specific information (type, frequency, and predicted impact) with data on haplotypes and on loss of heterozygosity.

SUBMITTER: Hainaut P

PROVIDER: S-EPMC5088513 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Somatic TP53 Mutations in the Era of Genome Sequencing.

Hainaut Pierre P Pfeifer Gerd P GP

Cold Spring Harbor perspectives in medicine 20161101 11

Amid the complexity of genetic alterations in human cancer, TP53 mutation appears as an almost invariant component, representing by far the most frequent genetic alteration overall. Compared with previous targeted sequencing studies, recent integrated genomics studies offer a less biased view of TP53 mutation patterns, revealing that >20% of mutations occur outside the DNA-binding domain. Among the 12 mutations representing each at least 1% of all mutations, five occur at residues directly invol ...[more]

PMID: 27503997

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Project description:BackgroundDriven by high throughput next generation sequencing technologies and the pressing need to decipher cancer genomes, computational approaches for detecting somatic single nucleotide variants (sSNVs) have undergone dramatic improvements during the past 2 years. The recently developed tools typically compare a tumor sample directly with a matched normal sample at each variant locus in order to increase the accuracy of sSNV calling. These programs also address the detection of sSNVs at low allele frequencies, allowing for the study of tumor heterogeneity, cancer subclones, and mutation evolution in cancer development.MethodsWe used whole genome sequencing (Illumina Genome Analyzer IIx platform) of a melanoma sample and matched blood, whole exome sequencing (Illumina HiSeq 2000 platform) of 18 lung tumor-normal pairs and seven lung cancer cell lines to evaluate six tools for sSNV detection: EBCall, JointSNVMix, MuTect, SomaticSniper, Strelka, and VarScan 2, with a focus on MuTect and VarScan 2, two widely used publicly available software tools. Default/suggested parameters were used to run these tools. The missense sSNVs detected in these samples were validated through PCR and direct sequencing of genomic DNA from the samples. We also simulated 10 tumor-normal pairs to explore the ability of these programs to detect low allelic-frequency sSNVs.ResultsOut of the 237 sSNVs successfully validated in our cancer samples, VarScan 2 and MuTect detected the most of any tools (that is, 204 and 192, respectively). MuTect identified 11 more low-coverage validated sSNVs than VarScan 2, but missed 11 more sSNVs with alternate alleles in normal samples than VarScan 2. When examining the false calls of each tool using 169 invalidated sSNVs, we observed >63% false calls detected in the lung cancer cell lines had alternate alleles in normal samples. Additionally, from our simulation data, VarScan 2 identified more sSNVs than other tools, while MuTect characterized most low allelic-fraction sSNVs.ConclusionsOur study explored the typical false-positive and false-negative detections that arise from the use of sSNV-calling tools. Our results suggest that despite recent progress, these tools have significant room for improvement, especially in the discrimination of low coverage/allelic-frequency sSNVs and sSNVs with alternate alleles in normal samples.

Dataset Information

Somatic TP53 Mutations in the Era of Genome Sequencing.

Publications

Somatic TP53 Mutations in the Era of Genome Sequencing.

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