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.

Figure 1.

TP53 mutation spectrum in human cancer. (A) TP53 locus on chromosome 17p13.1, showing main intron/exon structure (coding exons, red; noncoding exon 1 and 3′ UTR in exon 11, gray) and alternative exons 2/3, 9α, and 9β (orange). The position of Hp53int1 in intron 1 is shown, as well as the region of exon1/intron 1 overlapping with alternative exons 1 of WRAP53. All introns and exons are represented to scale, except intron 1. Blue bar, location of rearrangement breakpoints in osteosarcoma. Red arrows, position of the main (+1) and alternative (+40, +133) protein initiation sites. Green arrowheads, orientation of the coding sequences (TP53 is located on the minus strand of DNA, WRAP53 on the plus strand). (B, top) Codon distribution of mutations (missense, nonsense, and indels) in the coding sequence of TP53, based on mutation data derived from integrated genomic studies compiled in the Whole Genomes Resource (v73) of the COSMIC mutation database (cancer.sanger.ac.uk/cosmic/signatures), showing the position of major hotspots (>2% of all mutations) and mini hotspots (1%–2% of all mutations). (Bottom) Codon distribution of missense and nonsense mutations based on studies using conventional targeted sequencing approaches and compiled from the International Agency for Research on Cancer (IARC) TP53 mutation database (version R.13, 2008 [Olivier et al. 2010]). The IARC distribution is displayed as a mirror image of the COSMIC distribution. TAD1, TAD2, Transcriptional activation domains 1 and 2; PRR, proline-rich region; DBD, DNA-binding domain; JD, junctional domain; OD, oligomerization domain; CTD, carboxy-terminal domain.

Figure 2.

Variability and potential impact of TP53 haplotypes. (A) Linkage disequilibrium (LD) plot in the HapMap Panel for Caucasians, using 29 tag SNPs (tSNPs) for typing TP53 haplotypes (Garritano et al. 2010). The degree of LD is indicated by shades from black (strong LD) to white (no LD). This figure shows that most tSNPs in intron 1 and the TP53 promoter are in strong LD, forming a conserved haplotype block. (B) Model for loss of heterozygosity (LOH) at the wild-type (WT) allele depending on the interplay between “strong” (red) and “weak” (green) mutant haplotypes. (Top row) If the mutation (MT) occurs on a “strong” haplotype, its effects may dominate the residual WT “weak” haplotype, making LOH not compulsory. In all other haplotype combinations, LOH is required to eliminate a WT haplotype stronger than or equally strong as the one carrying the mutation.

Figure 3.

TP53 mutation classification algorithms. (A) The Poeta algorithm (left) (Poeta et al. 2007) classifies mutations occurring in the DNA-binding domain in two groups, disruptive (D) and nondisruptive (ND) mutations. D mutations either preclude the synthesis of p53 (nonsense mutations) or cause a structurally important amino acid change in the L2/L3 loops of the DNA-binding domain. Other missense mutations are classified as ND. (Right) D and ND mutations have different prognostic value for locoregional recurrence (LRR) in a cohort of 74 patients with squamous head and neck cancer (Skinner et al. 2012). (B) The Olivier/Hainaut algorithm (left) classifies mutations in three groups: mutations predicting a null p53 protein (non-missense), missense mutations in DNA-binding motifs (missense DBM), and missense mutations outside the DNA-binding motif (missense non-DBM). (Right) These three categories show different prognostic values for overall survival in a cohort of 1794 patients with breast cancer (Olivier et al. 2006).

Figure 4.

Prognostic value of TP53 mutation in breast and lung cancer driven by oncogenes of the EGF receptor family. (Left) Prognostic value of TP53 mutations in patients with HER2-enriched breast cancer subtype (Silwal-Pandit et al. 2014). (Right) Prognostic value of TP53 mutations in patients with adenocarcinoma of the lung with mutant EGFR (Clinical Lung Cancer Genome Project 2013).