Recommendations for Analyzing and Reporting TP53 Gene Variants in the High-Throughput Sequencing Era
Contract grant sponsors: Cancerföreningen i Stockholm, Cancerfonden; The Swedish Research Council.
For the TP53 Special Issue
ABSTRACT
The architecture of TP53, the most frequently mutated gene in human cancer, is more complex than previously thought. Using TP53 variants as clinical biomarkers to predict response to treatment or patient outcome requires an unequivocal and standardized procedure toward a definitive strategy for the clinical evaluation of variants to provide maximum diagnostic sensitivity and specificity. An intronic promoter and two novel exons have been identified resulting in the expression of multiple transcripts and protein isoforms. These regions are additional targets for mutation events impairing the tumor suppressive activity of TP53. Reassessment of variants located in these regions is needed to refine their prognostic value in many malignancies. We recommend using the stable Locus Reference Genomic reference sequence for detailed and unequivocal reports and annotations of germ line and somatic alterations on all TP53 transcripts and protein isoforms according to the recommendations of the Human Genome Variation Society. This novel and comprehensive description framework will generate standardized data that are easy to understand, analyze, and exchange across various cancer variant databases. Based on the statistical analysis of more than 45,000 variants in the latest version of the UMD TP53 database, we also provide a classification of their functional effects (“pathogenicity”).
The TP53 Gene 30 Years Later: From Simplicity to Complexity
Our increasing knowledge of the TP53 gene (MIM #191170) can stand as a paradigm for our evolving perception of a transcriptional unit over the last decade. The roots of the idea that one gene encodes one protein are to be found, with few exceptions, in the analysis of prokaryotes and lower eukaryotes. However, large-scale analyses using high-throughput methodologies have turned this concept upside down. Indeed, the latest issue of the ENCODE project suggested that 10–12 protein isoforms could be expressed by each human gene [Gerstein et al., 2012]. The TP53 gene is a model for this revolution in knowledge with the discovery of its complex architecture involving different mechanisms to transcribe at least eight mRNAs and translate up to 12 different protein isoforms (Table 1) [Bourdon et al., 2005]. The present review has three parts: in the first, we discuss how these novelties can affect the detection and analysis of TP53 variant status in human tumors. In the second, we discuss the reporting and classification of TP53 variants. In the third, we provide specific recommendations for the detection and reporting of TP53 variants.
| Common protein nameb | LRG_tc | LRG _pd | NCBI transcript | NCBI_Protein | Residues (kDa)e | Promoter | Major splicing eventf |
|---|---|---|---|---|---|---|---|
| Full-length p53, p53, p53α | t1 | p1 | NM_000546.5 | NP_000537.3 | 393/43.6 | P1/P1′ | I |
| Full-length p53, p53, p53α | t2g | p1 | NM_001126112.2 | NP_001119584.1 | 393/43.6 | P1/P1′ | I |
| p53β, p53i9 | t3 | p3 | NM_001126114.2 | NP_001119586.1 | 341/37.9 | P1/P1′ | II |
| p53γ | t4 | p4 | NM_001126113.2 | NP_001119585.1 | 346/38.5 | P1/P1′ | III |
| Δ40p53α, ΔNp53, p47 | t1 | p8 | NM_001276760.1 | NP_001263689.1 | 354/39.3 | P1/P1′ | I |
| Δ40p53α, ΔNp53, p47 | t2 | p8 | NM_001276761.1 | NP_001263690.1 | 354/39.3 | P1/P1′ | I |
| Δ40p53α, ΔNp53, p47 | t8h | p8 | NM_001126118.1 | NP_001119590.1 | 354/39.3 | P1/P1′ | I |
| Δ40p53β | t3 | p9 | NM_001276696.1 | NP_001263625.1 | 302/33.5 | P1/P1′ | II |
| Δ40p53γ | t4 | p10 | NM_001276695.1 | NP_001263624.1 | 307/34.1 | P1/P1′ | III |
| Δ133p53α | t5 | p5 | NM_001126115.1 | NP_001119587.1 | 261/59.6 | P2 | I |
| Δ133p53β | t6 | p6 | NM_001126116.1 | NP_001119588.1 | 209/23.7 | P2 | II |
| Δ133p53γ | t7 | p7 | NM_001126117.1 | NP_001119589.1 | 214/24.4 | P2 | III |
| Δ160p53α | t5 | p11 | NM_001276697.1 | NP_001263626.1 | 234/26.6 | P2 | I |
| Δ160p53β | t6 | p12 | NM_001276698.1 | NP_001263627.1 | 182/20.7 | P2 | II |
| Δ160p53γ | t7 | p13 | NM_001276699.1 | NP_001263628.1 | 187/21.4 | P2 | III |
- a Equivalence between the various identifiers used to describe the various isoforms of the TP53 gene.
- b Other names found in the literature for the various TP53 isoforms.
- c LRG identifiers for the various TP53 transcripts. A description of the LRG identifiers is described in the text and can be visualized at ftp://ftp.ebi.ac.uk/pub/databases/lrgex/LRG_321.xml.
- d LRG identifiers for the various TP53 proteins.
- e Theoretical molecular weight.
- f Major splicing events: I: classical splicing with all exons including exon 9; II: splicing event including exon 9 β; III: splicing event including exon 9 γ.
- g Transcript t2 has a deletion of 3 nucleotides at the beginning of exon 2.
- h Transcript t8 retains intron 2.
Although this review is focused on the TP53 gene, the recommendations and many issues discussed here are equally valid for other cancer genes, which may also require renewed investigation and variant effect reassessment.
TP53 Role in Cancer
The TP53 gene is not only the most frequently mutated gene in human cancer, it is also a gene that acts as through a signaling hub, integrating a plethora of upstream signals and orienting them to various effectors pathways (reviewed by [Vousden and Prives, 2009; Levine et al., 2011]). Furthermore, TP53 activities are largely dependent on the cellular context and the tissue of origin. Homozygote TP53 knockout mice do not die in utero, an attribute that is not found in other common tumor suppressor genes such as APC, PTEN, RB1, or BRCA1/2 [Taneja et al., 2011]. Furthermore, the predisposition to cancer in TP53 knockout mice starts largely after sexual maturity, suggesting that TP53, paradoxically, is not as essential as other tumor suppressors for individual survival [Kenzelmann Broz and Attardi, 2010; Jackson and Lozano, 2013].
Germ line TP53 variants are associated with predisposition to various types of hereditary cancer, including familial breast cancer, Li–Fraumeni syndrome and pediatric adrenocortical carcinoma [Custodio et al., 2013; Kamihara et al., 2014]. The association of a specific TP53 variant with pediatric adrenocortical carcinoma is currently unexplained and mouse models are urgently needed to assess its mechanisms in vivo. As reviewed by Donehower in this issue, knockin mice expressing different TP53 hotspot mutants display heterogeneous tumor phenotypes [Donehower, 2014]
The Structure of the TP53 Gene
The human TP53 gene organization as depicted over the past 20-plus years was simple: it produced a transcript containing 11 exons encoding a single protein of 393 amino acids [Soussi and May, 1996]. The structural organization of the gene is well conserved through evolution, although it comprises specific features that are still not fully understood. First, in humans and mice, the large intron (10 kb) between the noncoding exon 1 and exon 2 containing the first ATG codon encodes hp53int1, a small untranslated RNA of unknown function (Fig. 1) [Reisman et al., 1996]. A second particularity is the recently identified WRAP53 gene (MIM #612661) that partially overlaps the 5′ region of the TP53 gene in a head-to-head configuration (Fig. 1) [Mahmoudi et al., 2009]. Several WRAP53 transcripts overlap TP53 exon 1 as well as hp53int1, suggesting the possibility of regulation via the formation of double-stranded RNA. The consequences of this specific organization which is conserved in mammals are currently unknown. The fact that a similar gene is localized in the 5′ region of the TP73 gene (but not in the TP63 gene) suggests that it has some important regulatory function for TP53 and TP73 [Mahmoudi et al., 2009].
The full-length (393 aa) TP53 protein (TP53 also known as TP53 α or p1) translated from the major mRNA species initiated from promoter 1 (P1) upstream of exon 1 remains the most abundant isoform. The functional organization of the TP53 gene is more complex than previously thought: the NCBI's RefSeq database now contains 15 different pairs of TP53 transcript and protein records references due to the policy to associate only one RNA species to a single protein (Table 1 and Supp. Fig. S1). Thus, several mRNA species encoding more than one protein have been duplicated with different RefSeq NM-accession numbers and two protein isoforms are represented by multiple RefSeq NP-accession numbers. To solve this confusing situation, TP53 specialists have joined forces with the Locus Reference Genomic (LRG) Consortium, which provides stable reference sequences and a coordinate system for permanent and unambiguous reporting of disease-causing variants in genes related to any pathology [Dalgleish et al., 2010; MacArthur et al., 2014]. LRGs already cover 715 genes associated with noncancerous or cancerous diseases. Their records in Entrez Gene (http://www.ncbi.nlm.nih.gov/gene/) and Ensembl (http://www.ensembl.org/index.html) contain links to the corresponding LRGs. The UCSC Genome Browser Website (http://genome.ucsc.edu/) provides the LRG Regions track under the Mapping and Sequencing header and an LRG Transcripts track under the Genes and Gene Predictions header (January 2014). The joint effort resulted in a recently released stable TP53 reference sequence, LRG_321 containing the genomic sequence from human genome build GRCh37.p13 (ftp://ftp.ebi.ac.uk/pub/databases/lrgex/LRG_321.xml). We believe that its annotation with precise labels and coordinates of eight different TP53 transcripts (t1–t8) and 12 isoforms (p1 and p3 to p13) will be preferred to the RefSeq identifier pairs provided by the NCBI for genome build GRCh37.p13 (Table 1 and Supp. Fig. S1). Therefore, we will use LRG transcript and protein isoform numbers throughout this review. Without further specification, exon and intron numbering refers to transcript LRG_321t1; amino-acid positions to full-length LRG_321p1.
The TP53 gene produces all TP53 isoforms in a combinatorial manner using an alternative promoter, alternative splicing, and alternative translation start sites (Table 1 and Figs. 1 and 2) [Bourdon et al., 2005]. Their amino-termini are determined by the use of two different promoters each producing transcripts with two translation start sites, except for t8. Three different carboxy-termini can be generated by the exclusion or inclusion of two newly discovered exons, 9 β and 9 γ, localized in intron 9. Each of the four different amino-termini can be combined with each of the three carboxy-termini leading to 12 isoforms (Table 1 and Figs. 2 and 3). We will discuss this in more detail, starting with the amino-termini produced by P1 and P2 transcripts. The translation of the four P1-initiated TP53 mRNAs t1–t4 can begin at codon 1 or codon 40 leading to the expression of either full-length TP53 protein (TP53) or TP53 protein truncated of the first transactivation domain (Delta40p53). Furthermore, Delta40p53α proteins can also be encoded by alternatively spliced TP53 mRNA t8 still containing intron 2. It is important to note that intron 2 included in transcript t8 contains a stop codon that prevents expression of full-length p53. This alternative splicing of TP53 intron 2 has been described in several cell lines and normal human lymphocytes [Matlashewski et al., 1987; Ghosh et al., 2004]. The second group with different amino-termini contains TP53 isoforms encoded by three novel mRNAs: t5, t6, and t7. These transcripts are produced by the P2 promoter localized in intron 4 and probably extending to exon 3 and start from a novel transcription initiation site at the 3′ end of intron 4 (Figs. 2 and 3). They generate TP53 proteins with different amino-termini starting either at amino acid 133 or 160 (Delta133p53 and Delta160p53) Aoubala et al. (2011). Finally, each of the different amino-termini can be combined with a different carboxy-terminus. The alternatively spliced transcripts containing exon 9 β, t3 and t6, use two translation start sites each to encode 4 TP53 isoforms combining different amino-termini with the β carboxy-terminus (TP53β, Delta40p53β, Delta133p53β, and Delta160p53β). Similarly, t4 and t7 containing exon 9 γ encode four TP53 isoforms combining different amino-termini with the γ carboxy-terminus (TP53γ, Delta40p53γ, Delta133p53γ, and Delta160p53γ). Although expression at the physiological level of intron 2 containing t8 equivalents with exons 9 β and 9 γ remains to be demonstrated experimentally, these would probably encode the existing Delta40p53β and Delta40p53γ isoforms.
TP53 Variant Screening in Human Cancer: a Necessity
In human cancer, TP53 is the gene most frequently hit by somatic mutation events [Leroy et al., 2014a; Leroy et al., 2014b]. Questions concerning particular TP53 variant spectra can be raised in several malignancies, such as ovarian cancer or basal subtypes of breast cancer, as they display more frameshift and nonsense variants than other cancer types [Curtis et al., 2012]. We currently do not know if these variants are the consequence of a specific defect in the DNA repair system of these tumors or more so the result of counter selection for the expression of mutant TP53. Osteosarcoma is one of the few cancers displaying a high frequency of TP53 gene deletion, an observation already made in 1987 and confirmed more recently by whole genome analysis [Masuda et al., 1987; Barretina et al., 2010]. However, the basis of this gene deletion is currently unknown.
It is beyond the scope of this review to summarize the more than 20,000 publications describing correlations or lack of correlations between TP53 variants and various clinical parameters such as response to treatment, overall or relapse-free survival and many others. The fact that tumors with TP53 variants are associated with poorer prognosis has been repeatedly demonstrated for various types of cancer and recently confirmed in the pan-cancer study [Olivier and Taniere, 2011; Kandoth et al., 2013]. TP53 variants have been shown to have value for prognosis and treatment orientation in chronic lymphocytic leukemia (CLL) and thus TP53 screening has been recommended [Pospisilova et al., 2012; Malcikova et al., 2014]. The analysis of germline mutations in various cancer-prone families is also an essential clinical aspect of TP53 screening that goes beyond Li–Fraumeni syndrome [Kamihara et al., 2014]. TP53 germline mutations have also been observed in families at high risk of breast cancer and TP53 screening has been recommended for individuals diagnosed with early onset breast cancer at age 35 years or younger, with a negative BRCA1/BRCA2 test.
Analyzing TP53 variants in various types and subtypes of tumors will provide clues and permit the generation of working hypotheses on the pleiotropic functions of this gene and its products. The identification of variant hot and cold spots in specific cancer types is essential for a better understanding of both the structure/function relationship of the TP53 protein and tumor etiology. Combined with information about treatment outcome, this should improve the prognostic and predictive value of TP53 variant biomarkers.
TP53 Variant Spectrum and Isoforms
The first TP53 variants discovered affect the highly conserved domain of the protein, encoded by exons 5 to 8 of transcript t1 [Nigro et al., 1989; Takahashi et al., 1989]. These early observations caused a strong bias as the majority of the subsequent studies were performed by screening only the central region of the gene. This vicious circle led to the general belief that only a few variants were localized outside of the exons 5–8. Most sequencing studies have focused their analyses on exons encoding the central regions of the proteins (exon 5–8). More recent studies have shown that at least 10%–15% of TP53 variants are localized in exons 2–4 and exons 9–11 (Fig. 4) [Leroy et al., 2013; Leroy et al., 2014b]. Furthermore, the spectrum of these variants is different, as they consist mostly of small indels that usually lead to a TP53 null phenotype.
The majority of TP53 variants are localized in the central region of the full-length protein p1. Those localized in the amino- or carboxy-terminus will only affect the full-length TP53, sparing some isoforms (Fig. 4). Less than 1% of TP53 variants will miss the three Delta40 isoforms (p8, p9, and p10), but 7.4% and 18.4% of the variants will lead to the synthesis of intact Delta133 (p5, p6, and p7) and Delta160 (p11, p12, and p13) isoforms, respectively (Fig. 4). Variants in exons 10 and 11 are less frequent (2.4%) but they do not target β (p3, p6, p9, and p12) and γ (p4, p7, p10, and p13) isoforms (Fig. 4). Somatic variants in exon 11 containing the shared 3′UTR might deregulate the TP53 network [Li et al., 2013]. This could affect the expression of all transcripts. A few missense variants have been identified in exon 9 β and γ in several tumors.
The new transcripts and their isoforms raise important questions: Are any novel variant effects specific for the tumor they were observed in? Are these effects extendable and of importance to other types of cancer? What are the functional effects of a particular variant on each of the isoforms? Are all isoforms equally important for clinical decision making? Do variants targeting only a limited number of p53 isoforms lead to heterogeneous complexes with different consequences for the TP53 network?
Variants appearing before codon 133 or after codon 331 of full-length TP53 would lead to combined expression of variant and wild type (WT) TP53 protein isoforms from the same allele. Although loss of WT TP53 activity has been described, the biological consequences of the combined expression of both WT and variant isoforms in tumors are totally unknown. Because these isoforms can regulate gene expression, some may retain tumor suppressor activities in a mutant TP53 gene context, whereas others may confer gain of function. TP53 isoforms may thus explain inconsistencies in the different biological activities described for mutant p53. It is essential to further investigate this interplay with the goal of improving the prognostic and predictive value of p53 variant biomarkers.
TP53 Gene Analysis
Use of Prescreening Strategies
Prescreening strategies such as SSCP, DGGE, or dHPLC have been of tremendous value for the detection of variants in clinical samples (Supp. Table S1). Although their specificity is variable, several offer better sensitivity than direct sequencing to detect variants in tumor tissue heavily contaminated with normal cells. These strategies can be easily extended to incorporate the new target regions. However, the spectacular decrease in the cost of conventional and next-generation sequencing (NGS), as well as automation and use of high-throughput capillary electrophoresis have alleviated the need of prescreening. It may even be advantageous to avoid the latter in order not only to get the best specificity but also to reduce costs and delays, which are important considerations in routine clinical practice.
Immunohistochemistry
It is commonly accepted that somatic TP53 missense variants lead to the accumulation of the TP53 protein in the tumor cell nucleus. Thus, tumor staining using various TP53 monoclonal antibodies has developed as a surrogate for TP53 variant analysis [Bartek et al., 1991]. Several thousand articles describing TP53 staining in a wide variety of tumors have been published and several reviews have addressed the various problems associated with this indirect screening technique [Hall and Lane, 1994; Hall and McCluggage, 2006]. Reviewing these studies is beyond the scope of this article, but it has been largely demonstrated that the relation between TP53 accumulation and TP53 variants is not straightforward. Although truncated TP53 proteins resulting from nonsense and frameshift variants cannot be assessed via immunohistochemistry, it has been repeatedly reported that some tumors with missense variants could also be negative for TP53 accumulation [Casey et al., 1996; Alsner et al., 2008; Gluck et al., 2012]. In contrast, tumors without TP53 variants have been reported with TP53 protein accumulation. We strongly advocate against TP53 staining as a screening methodology for TP53 variants. Any possible clinical value for the analysis of TP53 accumulation per se, without inferring the origin of such accumulation, remains to and should be determined, but this issue should not be conflated with TP53 variant screening. It is also important to consider the monoclonal antibody used for the detection of TP53 accumulation. Most commercialized antibodies recognize an epitope localized in the amino-termini of the proteins (1–40) and will thus miss most isoforms [Legros et al., 1994; Tenaud et al., 1994]. Because of the presence of immunodominant epitopes in the TP53 proteins, polyclonal sera raised against human TP53 are highly biased toward the amino-terminus. Because several TP53 isoforms have different subcellular localizations, it would be interesting to investigate, via IHC using a panel of antibodies, any possible associations between their subcellular localization (nuclear, cytoplasmic, or speckled staining) and disease outcome, and any possible role for subcellular localization in the stratification of cancers into subtypes [Ghosh et al., 2004].
Variant Arrays and Sensitivity of TP53 Analysis
Variant arrays mainly detect known sequence alterations. The TP53 variant spectrum is quantitatively and qualitatively very heterogeneous across the various types of cancer. In colorectal carcinoma, the high frequency of transitions localized at methylated CpG dinucleotides leads to a high clustering of variants at codons 175, 248, 273, and 285. This clustering is also observed in astrocytomas and glioblastomas, but not for other types of cancer. Using the 45,000 TP53 variants included in the database, it is possible to calculate a sensitivity index to establish the number of variants that need to be screened for in order to attain a given sensitivity (Fig. 5). Looking at all cancers in the TP53 database shows that 1,400 variations must be assessed to reach a sensitivity of 90% (Fig. 5). However, when looking at any one type of cancer, the variant screening number can vary widely, from 450 in colorectal carcinoma to 1,040 in breast carcinoma, depending on variant spread and the heterogeneity of the various mutation events. Several commercial arrays are currently available for the detection of TP53 variants but none attain a sensitivity higher than 50%. Arrays will have to be redesigned to cover new target regions. Whether an array interrogating every position to cope with the highly scattered nature of TP53 variants would be more effective than sequencing in terms of specificity, sensitivity, or cost is still an open question.
Sample Origin
Formalin fixation plus paraffin embedding (FFPE) is one of the most widely practiced methods of clinical sample preservation and archiving. It has been estimated that there are over 400 million FFPE tissue samples archived in tissue banks worldwide, a gold mine for genomic analyses [Sah et al., 2013]. Although changing therapeutic protocols have reduced the clinical value of some collections, their intrinsic value is inestimable for the definition of variant profiles. These samples will be essential for novel studies such as the 10K project, which is focused on sequencing more than 100,000 tumor specimens (http://news.sciencemag.org/biology/2013/03/ready-more-10000-cancer-genomes-projects). However, DNA extraction from FFPE samples is challenging. Common problems such as formaldehyde cross-linking, degradation, and mixing of single-stranded and double-stranded DNA result in fragmented DNA of variable quality. The age of the sample and the quality of storage have also been shown to be important parameters for DNA quality. Compared with the frozen tissue, sequencing DNA extracted from FFPE samples requires more accurate quality controls; indeed several studies have reported artifact variants associated with this type of material [Williams et al., 1999; Marchetti et al., 2006; Soussi et al., 2006].
Although novel methodologies are available to improve the quality and the yield of DNA extracted from FFPE tissue, the risk of sequencing artifacts will remain greater than when using DNA from frozen tissue. For NGS, it will be essential to develop accurate algorithms to distinguish method- or sample quality-related sequencing background errors before assessing variant calling procedures. Another key point with FFPE tissue is the size of the specimen. Large tissue samples, harvested for example during surgery, permit the detection of most tumor variants, including those in the various subclones that sustain tumor heterogeneity. In contrast, PFFE samples, often obtained from local biopsies, can be very small and thus may provide only a limited view of the various genetic alterations. This aspect is not genuinely problematic for somatic TP53 variants that occur early during transformation because they spread throughout the tumor. However, it is problematic for somatic variants arising by mutation events at later stages and thus only present in subclones that may not be represented in a small specimen of the tumor.
The Genetic Material
Both genomic DNA and cDNA derived from mRNA have been used to infer TP53 mutational status in human tumor or cell lines. Sequencing genomic DNA allows unequivocal identification of any primary change, also in promoter and intronic regions. Sequencing cDNA limits variant detection to transcribed regions, but will elucidate the effects of primary changes at the RNA level supporting better predictions of effects at the isoform level. As discussed by Leroy et al. (2014a), cDNA sequencing is associated with potential artifactual results as point variants that affect splicing lead to abnormally spliced RNA species usually quoted as deletions.
Screening and Analyzing the TP53 Gene in the Postgenomic Era
The discovery of the various TP53 isoforms, the two novel exons localized in intron 9 and the promoter in intron 4 suggests that the conventional screening strategy is inadequate and must be extended with these regions [Bourdon et al., 2005] (Fig. 6). The 3′UTR of the TP53 gene forms another target for potential alterations. In patients with B-cell lymphoma, somatic variants in the 3′UTR disrupt the interaction between TP53 mRNA and miR-125b, possibly resulting in deregulation of the TP53 network [Li et al., 2013]. These new functional elements raise many questions, some of which can be answered using new technology: What is the spectrum of variants hitting them? Are all transcripts and isoforms expressed in all tissues and relevant for all types of cancer? What are the qualitative and quantitative effects of variants on each of the transcripts and isoforms?
A more complete picture might emerge using RNAseq with NGS and accurate bioinformatics analysis. Although historically, splicing TP53 variants have been thought to occur infrequently, recent large-scale analyses have suggested that they may be underestimated and may represent 2%–4% of total variants. Variants localized at the border of exons and predicted to be synonymous have been shown to affect normal splicing [Leroy et al., 2014a]. The combination of DNA and RNA analysis should permit the detection of variants and identification of normally spliced and all aberrant mRNA species resulting from them. The expression pattern of the various TP53 transcripts is likely tissue-specific, necessitating clear definition for each tissue sample and annotation of all relevant experimental conditions. Whether or not variants can qualitatively or quantitatively modify this complete transcript and isoform profile is currently unknown. Identification of transcript variants caused by splice defects may also be possible depending on how efficiently the new splice site and alternative sites are used, and the sequencing coverage. Such studies will be very informative as they will establish if any TP53 proteins, abnormal or normal, can be potentially translated or if these tumors should be considered as TP53 null not expressing TP53 gain of function variants. RNAseq will also be very useful for the identification of exonic splicing enhancer or exonic splicing silencer regions, which modulate splicing [Wang and Cooper, 2007; Sauna and Kimchi-Sarfaty, 2011]. Their positions could be inferred from exonic variants hitting them and their consequences on TP53 splicing. This would help to establish possible associations between their alteration and gene misregulation in human cancer.
Toward an Adequate TP53 Sequence Analysis Pipeline
Although several thousand tumor genomes from various types of cancer have been sequenced, data for intronic sequences, including the two novel exons of TP53, have not been analyzed and are not easily accessible. This is due to the filtering pipeline used by most studies, which causes variants in newly discovered exons awaiting annotation in the various databases to be missed (Fig. 7). Current strategies are largely biased toward coding regions, with the separation of the various variants in four tiers as follows: tier 1: variants altering coding sequences (nonsynonymous or synonymous) splice site, or noncoding RNA; tier 2: variants targeting conserved or regulatory sequences; tier 3: variants occurring in nonrepetitive regions of the human genome, including introns; and tier 4: variants occurring in repetitive noncoding regions [Ding et al., 2010]. In most studies, only tiers 1 and 2 are used to profile the mutational landscape of tumors. Mining various databases, which are not always freely available, for variants in specific regions to analyze raw sequence data will require considerable time and expertise. Exomic analysis via NGS is also currently biased since (commercial) exome capture kits must be upgraded to include β and γ exons.
The classical NGS sequence analysis pipeline for clinical samples has three phases (Fig. 7). After the base calling and variant calling phases, the variant filtering phase includes numerous steps requiring access to external references. Several tools can be used to annotate variants on all different transcripts. The last phase could use the LRG for unambiguous descriptions of variants at the different levels contributing to harmonization of TP53 variant reporting as shown below.
Reporting TP53 Variants
A Note on Terminology
For more than 15 years, the Human Genome Variation Society (HGVS) has provided guidelines for variant terminology and nomenclature. The HGVS recommends the use of the term “variant” instead of “mutation,” “SNP” or “polymorphism” for sequence variants in general, regardless of their functional consequence or tissue of origin (see http://www.hgvs.org/mutnomen/). We would like to suggest the TP53 community to embrace this recommendation for the following reasons.
Originally, SNP described a germ line variation that exists at a frequency of at least 1% in the general population (http://www.ncbi.nlm.nih.gov/books/NBK21088/). Such variations are the roots of diversity in species and tremendously useful as markers for genetic studies. Created in 1998, the dbSNP database maintained by the NIH keeps track of SNPs (http://www.ncbi.nlm.nih.gov/SNP/). In the literature, the term SNP has obtained an additional meaning associated with low or very limited risk on disease or tumor formation. Since 2011 (build 134), dbSNP started accepting submissions of germ line and somatic variations associated with various types of diseases and changed its name to “database of Short Genetic Variation” keeping the dbSNP acronym. Several frequent TP53 variants (e.g., rs28934578) are included in dbSNP, but other hot spot variants are missing, whereas rare somatic variants can be found. This heterogeneity caused by biased dbSNP submissions is misleading, as it does not reflect the true occurrence and frequencies of TP53 variants. Therefore, without further distinction, we can no longer assume that variants in dbSNP are associated with the lack of effect on disease and tumor characteristics. The mix of neutral and disease-causing variants in dbSNP has led to confusion and ambiguities in the TP53 field and many others. The use of “SNP” and “polymorphism”, which is normally associated with low or very limited risk on disease or tumor formation, for all variants in dbSNP could be detrimental for various types of analysis, potentially leading to the wrong clinical diagnosis. It is also one source of discrepancies between TP53 variant databases, fueling discussions about variants being “true SNPs,” “natural SNPs” or disease-causing “mutations.” As all of them can no longer be regarded to meet the original definition, it would be better to refer to them as “dbSNP entries”.
Classification of TP53 Variants
Although all TP53 germ line variants have been detected in tumors, there is a clear need to distinguish them from somatic variants, as TP53 variants may have different effects in normal tissue and tumors because of the various complex roles of TP53 isoforms. We would like to propose simplifying variant descriptions by indicating all variants observed in the germ line as a “germ line variants” and variants observed in tumors, but not present in normal tissue, as a “somatic variants.” If normal tissue has not been examined, variants observed in tumor tissue could be labeled as “variant detected in tumor.” Thus, germ line variants detected in one individual can be described in others as “detected in tumor” or as somatic variants. Many clinical geneticists are using the five-class system for germ line variants (Plon et al., 2008]. We suggest using the same classification for somatic variants ranging from Class 5 (has functional consequences, “pathogenic”) via Class 3 (unknown, Variant of Uncertain Significance [VUS]) to Class 1 (benign). Variants previously described as “passenger” or “hitch-hiking mutation” could be assigned to Classes 1–3, depending on supporting evidence. Clearly, this classification describes the functional consequences of the variant in isolation. Future refinement may be required to describe modifier effects when different variants occur in combination on the same or different alleles. Although the predicted functional consequences for germ line and somatic variants at the RNA and protein level will be the same, their clinical effects may differ. Additional classification may be necessary to distinguish between increased predisposition to tumor formation for the first and potential effects on tumor progression, prognosis, and treatment outcome for the latter.
Correct Sequence Variant Nomenclature
To keep up with our increasing knowledge of gene architecture, the HGVS has regularly published recommendations and guidelines for variant nomenclature [Cotton and Malcolm, 1991; Claustres et al., 2002; Cotton et al., 2008; Auerbach et al., 2011]. The latest update of these recommendations, compiled by den Dunnen and Antonarakis (2000), is available on the HGVS Website (http://www.hgvs.org/mutnomen/). Nonetheless, the literature is plagued with fancy—but meaningless—or incomplete variant descriptions. During TP53 variant database curation, we noticed a large degree of heterogeneity in published TP53 variant descriptions with less than 20% following the official nomenclature despite numerous contacts with editors and publishers. Many “exotic” nomenclatures often hampered the accurate identification of variants. Furthermore, numerous studies contained typographical errors or incorrect reference sequences due to manual manipulation of the data. Several years ago, we contacted more than 20 journal editors to discuss the use of correct variant descriptions with them. Although they acknowledged the problem, currently merely four journals have initiated efforts to solve this for new manuscripts by making the use of HGVS variant nomenclature mandatory. Thus, we fear that incorrect variant descriptions, mostly related to manual sequencing, will remain permanently in existing literature. Ultimately these will be diluted by correct variant descriptions from computerized NGS data analyses including tools describing variant according to the official nomenclature. Although much less, mistakes can still occur because of the heterogeneous gene nomenclature and numbering systems, combined with the very high number of automatically processed genes. We have noticed a number of “scrambled” TP53 variant descriptions, mixing both the coordinates of the full-length protein and those of a particular isoform in a single list. Thus, harmonization of TP53 variant reporting is urgently needed to accurately perform comparative cross-study analyses, and fully appreciate their pleiotropic effects and establish their relevance in clinical practice.
Numerous recent publications describe TP53 variants only at the protein level, such as p.R175H or p.R248W. This trend is usually associated with the use of commercial or custom-made arrays specific to cancer gene variants. Using protein variant descriptions is highly confusing because the true genetic event cannot be correctly inferred. Because of the codon degeneracy, several mutation events can lead to the same amino-acid substitution. HGVS variant nomenclature in combination with the stable LRG_321 reference sequence can generate unambiguous TP53 variant descriptions at the DNA level. In case no other variants are described, one-time specification of the LRG_321 reference sequence suffices to use transcript and protein isoform numbers. Thus, the TP53 hotspot variant LRG_321p1:p.R249S (in short p1:p.R249S), which can result from two different transversion events should have been described as either t1:c.747G>T or t1:c.747G>C. Unambiguous variant descriptions at the DNA level ensures that information can easily transferred between various sources, for example, from publications to databases, or from one database to another. The LRG annotation supports automatic conversion of these descriptions to define the consequences of any variant for the eight transcripts and 12 protein isoforms. They would also support reconstruction of the sequence observed as input for bioinformatics tools predicting effects on splicing and other downstream processing at the RNA and protein level. For instance, the t1:c.314G>T variant previously predicted to result in amino-acid substitution p.(G105D) creates a new donor splice site (CAGGGCAGC to CAGgtcagc). This splices out the end of exon 4 and intron 4, leading to an in-frame deletion of the end of exon 4. The new transcript would be translated into a p53 protein with its second conserved domain deleted as observed in breast tumors (J. C. Bourdon, personal communication). Describing and reporting these changes at the RNA and protein level as t1:r.313_375del and p1:p.G105_T125del would help to train bioinformatics tools and get better predictions.
Variants in dbSNP
In the human population, hundreds of dbSNP entries describe variants in the TP53 gene or in its vicinity and several haplotype blocks have also been identified [Mechanic et al., 2007; Phang et al., 2011; Ortiz-Cuaran et al., 2013]. A large number of studies have focused on the association between common TP53 germ line variants and cancer risk (reviewed in [Whibley et al., 2009]). Several dbSNP entries such as rs78378222 (t1:c.*1175A>C, localized in the 3′UTR of the gene and responsible for changing the AATAAA polyadenylation signal to AATACA, RNA change: r.*1175a>c) or rs17878362 (t1:c.96+41_97–54del, 16 bp deletion in intron 3) have been associated with an increased risk of cancer but more studies are needed to establish any possible clinical value [Stacey et al., 2011].
Classification of dbSNP Entries Associated with TP53 Amino-Acid Changes
Combing the literature, we have identified 14 dbSNP entries (previously called “natural SNPs”) associated with amino-acid changes in the TP53 protein (Table 2 and Supp. Table S2). Unfortunately, the literature search did not reveal the princeps publications defining several of these variants. We have classified the variants described in these dbSNP entries as Class 1 (benign, previously “certified”: “C”), Class 3 (“uncertain”: “U”), or Class 5 variant (“mutation”: “M”) (Table 2 and Supp. Table S2).
| SNPb | cDNA variantc | Protein variantc | Common name | Database frequencyd | Activitye | Population analysisf | Classg |
|---|---|---|---|---|---|---|---|
| rs1800371 | t1:c.139C>T | p1:p.P47S | p.P47S | –h | Wt | 5 | 1 |
| rs1042522 | t1:c.2151>G | p1:p.P72R | p.P72R | –h | Wt | >10 | 1 |
| rs11540654 | t1:c.329G>T | p1:p.R110L | p.R110L | 58/3 | Null | 1 | 5 |
| rs11540654 | t1:c.329G>C | p1:p.R110P | p.R110P | 25/1 | Null | 0 | 5 |
| rs72661117 | t1:c.550G>A | p1:p.D184N | p.D184N | 36/0 | Wt | 0 | 3 |
| rs35163653 | t1:c.649G>A | p1:p.V217M | p.V217M | 14/0 | Wt | 2 | 1 |
| rs72661119 | t1:c.787A>G | p1:p.N263D | p.N263D | 8/0 | Wt | 0 | 3 |
| rs55832599 | t1:c.799C>T | p1:p.R267W | p.R267W | 65/2 | Null | 0 | 5 |
| rs17849781 | t1:c.832C>G | p1:p.P278A | p.P278A | 48/1 | Null | 0 | 5 |
| rs55819519 | t1:c.869G>A | p1:p.R290H | p.R290H | 47/7 | Wt | 0 | 3 |
| rs56184981 | t1:c.932A>G | p1:p.N311S | p.N311S | 2/0 | Wt | 0 | 3 |
| rs17882252 | t1:c.1015G>A | p1:p.E339K | p.E339K | 1/0 | Wt | 4 | 1 |
| rs35993958 | t1:c.1079G>C | p1:p.G360A | p.G360A | 3/0 | Wt | 3 | 1 |
| rs17881470 | t1:c.1096G>T | p1:p.S366A | p.S366A | 4/2 | Wt | 1 | 1 |
- a An extended version of this table is available (Supp. Table S2).
- b Only dbSNP entries describing exonic changes resulting in amino-acid substitutions. The database document contains a full list of all TP53 dbSNP entries (Supp. Table S3).
- c Description of variants using the LRG_321 reference sequence.
- d Frequency of each variant in the 2014 issue of the UMD TP53 database. The two numbers correspond to somatic and germline variants respectively.
- e Functional activity of each variant defined by Kato et al. (2003) and from the UMD TP53 database (Hamroun et al., 2006).
- f Number of large-scale sequencing projects that have described this SNP (data from http://www.ncbi.nlm.nih.gov/SNP/, build 139).
- g Classification of each dbSNP entry as benign, Class 1 (1), VUS, Class 3 (3) and deleterious, Class 5 (5). Difference between 1 and 3 is based on population analysis. See text for more details.
- h These dbSNP entries have never been included in the TP53 database.
rs1042522 (t1:c.215C>G, p1:p.P72R, previously described as p.R72P due to a reference sequence based on the other allele) and rs1800371 (t1:c.139C>T, previously: p.P47S) are the two most frequently detected exonic TP53 variants. They have been extensively analyzed and their status as a Class 1 germ line variant is clear (Table 2 and Supp. Table S2); they will thus not be further discussed here.
Four variants in dbSNP, rs11540654 (t1:c.329G>C, previously: p.R110P and t1:c.329G>T, previously: p.R110L), rs55832599 (t1:c.799C>T, previously: p.R267W), and rs17849781 (t1:c.832C>G, previously: p.P278A) have been detected in the germ line of cancer families (Table 2 and Supp. Table S2). Their dbSNP records show that two of them are very rare (rs11540654 [p.R110L and p.R110P]) and no population frequency data are available for the two others. These four variants have no transcriptional activity and lack proapoptotic function [Kakudo et al., 2005; Soussi et al., 2005; Wang et al., 2013]. It is therefore likely that these dbSNP entries represent rare deleterious germ line variants (Class 5). Their detection in various types of cancer described in multiple entries in the UMD TP53 database suggests they are also Class 5 somatic variants (Table 2 and Supp. Table S2).
rs72661117 (t1:c.550G>A, previously: p.D184N) represents a dbSNP entry of a somatic variant, which has not been detected in the population. Found in 36 tumors in the UMD TP53 database, its frequency is higher than the passenger mutation background, but it is not significantly associated with dubious studies. Variant p.D184N does not display any obvious deficiencies in its transcriptional activity. It is located close to Ser183, a residue phosphorylated by Aurora B, but not within its consensus sequence [Gully et al., 2012]. More information about potential effects on TP53 posttranslational modification is needed to justify another classification than somatic Class 3.
dbSNP entry rs35163653 (t1:c.649G>A, previously: p.V217M) is a very rare germ line variant described in two independent populations. The UMD TP53 database contains 14 descriptions of this variant being somatic, but mostly from articles containing dubious data [Edlund et al., 2012]. Although this variant can be considered a Class 1 benign germ line variant, further research is needed to assess its functional consequences in tumors.
The status of rs55819519 (t1:c.869G>A, previously: p.R290H) is more ambiguous. This variant changed the G of a CpG dinucleotide compatible with the deamination of the methylated cytosine on the other DNA strand. No loss of activity has been associated with this variant but it is localized close to posttranslationally modified residues K291 (ubiquitination site) and K292 (ubiquitination and acetylation site). It has been described 47 times in the UMD TP53 database, including germ line variants in seven independent cancer families. No population data are available, suggesting this is an extremely rare germ line variant (Class 3). The impact on TP53 function remains to be investigated, so the somatic variant has to be classified as Class 3 for the moment. The reciprocal transition caused by deamination of methylated cytosine t1:c.868C>T appears 21 times in the TP53 variant database, does not lead to TP53 loss of function and can be assigned to Class 1.
The three dbSNP entries rs17882252 (t1:c.1015G>A, previously: p.E339K), rs35993958 (t1:c.1079G>C, previously: p.G360A), and rs17881470 (t1:c.1096T>G, previously: p.S366A) share similar observations. They are present at very low frequency in the UMD TP53 database, do not affect TP53 activity and have been validated in multiple population analyses. Therefore, they can be reasonably considered as Class 1 germ line and somatic variants.
The two dbSNP entries rs56184981 (t1:c.932A>G, previously: p.N311S) and rs72661119 (t1:c.787A>G, previously: p.N263D) are very infrequent in the UMD TP53 database, do not affect TP53 activity and lack population information. Therefore, they have been assigned to Class 3.
Considered in its totality, this analysis indicates that several TP53 dbSNP entries are indeed somatic Class 5 variants, but others need more verification. This “pollution” of dbSNP with variants not belonging to the low-risk Classes 1 and 2 clearly could result in removal of deleterious variants from NGS data when crudely filtering against the whole database. The problem becomes more consequential when the database is used by private companies to infer disease risk. For example, rs55819519, discussed above, has been used to infer potential risk and labeled as “The change R<>H is uncommon and the homozygous form could be significant.” Such annotation in dbSNP is extremely alarming and only curation of dbSNP and careful use of its data can prevent problems. An annotated list of all TP53 dbSNP entries is available in the Supp. Table S3.
Annotating Variants in the TP53 Gene
TP53 variants can be annotated independently with variant descriptions, as well as their consequences at the RNA and protein level using information from the TP53 database (Fig. 8). Specific information, such as the protein domain location, posttranslational modifications, phylogenetic conservation, or properties associated with the WT residue provides insights on the importance of the residue. Further information includes the frequency of the variant in the database, a functional analysis and predictions of deleterious amino-acid substitutions using popular algorithms. Any analytical pipeline for calling TP53 variants can easily incorporate these data (Supp. Table S4). Four examples below demonstrate how LRG coordinates can help to illustrate effects on different transcripts and isoforms.
chr17:g.7578406C>T (t1:c.524G>A) is a hotspot variant that leads to the expression of the p.R175H TP53 protein. This variant, localized in exon 5, hits all eight TP53 mRNAs and 12 TP53 isoforms. The changes of a specific transcript and isoform can be described using their t and p numbers (e.g., t5:c.128G>A resulting in p5:p.R43H and p11:p.R16H). For t1:c.524G>A, the analysis is straightforward and indicates that this variant inactivates TP53 tumor suppressive functions.
chr17:g.7579358C>A (t1:c.329G>T; p1:p.R110L) is a good example of a frequent somatic Class 5 variant that targets only a subset of TP53 isoforms. Indeed, as it is localized in exon 4, it does not impair all delta133 and delta160 TP53 isoforms. Although this variant impairs the activity of the full-length protein, its biological consequences for the various isoforms are unknown.
chr17:g.7579312C>T (t1:c.375G>A) predicted to be a synonymous variant has been often described as p.T125T. The nucleotide substitution is localized at the end of exon 4 and has been shown to lead to aberrant splicing, affecting 5 transcripts produced by the P1 promoter [Leroy et al., 2014a]. If a normally spliced transcript with the synonymous codon is expressed, its protein should now be described as p1:p. = . Transcripts t5, t6, and t7, transcribed from the internal P2 promoter in intron 4, should be normal.
chr17:g75794222C>T (t1:c.265C>T; p1:p.P89S) is a nonsynonymous variant reported 28 times in the 2014 version of the UMD TP53 database. This variant does not display a significant loss of activity. Several prediction programs do not identify it as deleterious (Fig. 8). Twenty-two of these 28 variants were described in a single publication that has been shown to be artifactual [Patocs et al., 2007; Edlund et al., 2012]. The remaining six variants were found in tumors that contained multiple TP53 variants. This variant is therefore a typical example of an artifactual result, which has been tagged in the database.
Assessing the Functional Effects of These Variants
An in-depth discussion of the assessment of the TP53 variant effects is beyond the scope of this review, but we would like to emphasize that high quality data are essential if variant information is to be used for clinical decisions. The identification of the founder variant p.R337H in Brazil is a perfect example of this reciprocal complementarity between clinical and basic research [Ribeiro et al., 2001]. LRG_321t1:c.1010G>A (p.R337H) is a germ line variant associated with a high predisposition to pediatric adrenocortical carcinoma, with prevalence reaching 5 per 1,000 in certain districts of the state of Paraná in Brazil [Custodio et al., 2013]. This variant, localized in the oligomerization domain of exon 10, does not target all TP53 isoforms. Functional analyses have shown that full-length TP53 protein variant is transcriptionally active but somewhat sensitive to changes in pH [DiGiammarino et al., 2002].
TP53 variants have been classified using multiple criteria [T. Soussi et al., unpublished results] (Fig. 9). Variants included in the TP53 database were categorized in three classes: (1) Class 5—variants having functional effects (“pathogenic”); (2) Class 4—variants likely having functional effects (“likely pathogenic”); and (3) Class 3—VUS. Although the number of VUS is high, most are infrequent and correspond to 9% of the total number of variants in the database. On the other hand, Classes 5 and 4 variants correspond to 76% and 14% of the total number of variants, respectively.
Multiple generic bioinformatics tools developed to assess variant effects have often used TP53 variants as a paradigm to check their specificity and sensitivity, both of which rarely exceed 80%. Most of these tools are only efficient for variants whose functional effects were otherwise obvious with simple criteria such as frequency or association with a loss of activity. We believe that it will be impossible to improve their rate of detection substantially in the future. The next generation of tools should be tailored for each gene by including information about the disease mechanism, specific data related to transcript and protein structure and function as well as information related to variant frequency and interactions between variants. Collecting this information in gene variant databases (locus-specific databases) will be invaluable for TP53, but also for other genes.
Recommendations
- The complete gene (promoter, exons, and introns), including the region that overlaps with the WRAP53 gene should be screened at the DNA level in different tissues and tumors to identify variants of importance. This unbiased analytical approach is vital and irreplaceable for defining unambiguously the regions of TP53 that are of importance in human cancer and generating important working hypotheses to understand the tumor suppressive function of TP53 and it various isoforms, including those encoded by the new β and γ exons. Although conventional approaches can be adapted to cover the new regions, new strategies using NGS platforms might be more effective, at least for research purposes.
- The terms “mutation” and “SNP” are ambiguous and should not be used. The HGVS recommends using the term “variant,” regardless of its origin or frequency. Variants can be distinguished according to their origin and their functional consequences. Germ line variants are those inherited or arising de novo before fertilization. In the context of cancer, somatic variants are observed in tumor cells, but not in normal cells. Variants detected in tumor material should not be labeled as somatic, unless their absence in normal cells of the same individual has been confirmed. Without this confirmation, variants can be annotated as “detected in tumor”
- We would like to propose describing functional consequences at the molecular level using the same classification system for germ line and somatic variants. Both germ line and somatic variants can have functional consequences (“pathogenic variants”) or not (“benign variants” including so-called “somatic passenger mutations”). In case insufficient evidence exists, variants are classified as VUS. The five-class system including the intermediate terms “Likely having functional effects” (Class 4) and “Likely benign” (Class 2) is already applied for calculations of cancer susceptibility risks of inherited variants and takes information about recurrent somatic variants into account [Plon, 2008]. We recommend specification of variant origin to assess potential differences between the functional consequences of germ line and somatic variants. This may help to reconcile and refine different classifications and help translating this information into a clinical outcome probability score.
- All researchers and clinicians working on TP53 are recommended to:
- Specify the reference sequences used to describe data at the different levels (genome build, transcript and protein reference sequence accession numbers and version numbers). For data standardization, the stable Locus Reference Genomic sequence LRG_321 is preferred. In case, RefSeq gene or transcript records are used, their version numbers should be included.
- Describe germ line and somatic variants according to the HGVS sequence nomenclature guidelines. LRG_321t1 should be used at the coding DNA and RNA levels; the full-length TP53 protein LRG_321p1 at the protein level. In case variants in multiple genes are described, the full format (e.g., LRG_321t1:c.215C>G, LRG_321t1:r.215c>g, LRG_321p1:p.P72R) is recommended. In publications, after specification of the reference sequence record, the transcript or protein number is sufficient (e.g., t1:c.215C>G, t1:r.215c>g; p1:p.P72R). To report changes in the P1 and P2 promoter sequences, either chromosomal positions or positions relative to transcripts should be used. In general, promoter studies indicate the position of the first nucleotide upstream of the transcription start site as −1, counting backward to the position of transcription factor binding sites. The HGVS nomenclature would indicate this as t1:c.-203. A more informative description format similar to the one used for intron variants still awaits HGVS approval. In this format, t1:c.-202-u1 contains the start of the first exon and the prefix u indicating an upstream location which upon removal results in the traditional position −1.
- Support further evaluation of the classification of variants and their clinical consequences by submitting variant data to publicly available gene variant databases.
- In discussions about functional effects of variants on other TP53 transcripts and protein isoforms, changes at the coding DNA, RNA, and protein levels can be indicated using the corresponding LRG_321 transcript and protein numbers. Observed effects on splicing should be described at the RNA level using t1:r. Predicted effects on RNA and protein should be between parentheses (e.g., t1:r.(215>g); p1:p.(P72R)).
Conclusions
Novel large-scale analyses have confirmed and extended the unchallenged leadership of TP53 as the most frequently mutated gene in human cancer [Kandoth et al., 2013; Lawrence et al., 2014]. The value of TP53 variants as predictive or prognostic markers in various types of cancer has been extensively analyzed. The repeatedly shown association of Classes 4 and 5 TP53 variants with a poor prognosis in CLL resulted in published recommendations for TP53 screening [Malcikova et al., 2014]. In other tumor types, novel studies using molecular classification of cancer subtypes have also associated Classes 4 and 5 TP53 variant with poor prognosis. All these research efforts point toward a need to standardize the analysis of TP53 alterations so that their clinical significance in all subtypes of cancer can be clearly established. The high frequency of TP53 alterations across all types of cancer indicates that they can be useful as molecular biomarkers for monitoring tumor progression. Indeed, several recent studies have shown that TP53 variants can be detected in tumor DNA circulating in the plasma of patients with breast or ovarian cancer [Murtaza et al., 2013; Bettegowda et al., 2014]. Methods that detect TP53 alterations with high sensitivity even in the presence of large amounts of normal DNA have been developed. Their specificity may be disappointing as they target only a few hotspot regions. Advances in genomic methodologies will alleviate the various sensitivity and specificity issues but only intricate knowledge of the significance of the various alterations of the target gene will lead to efficient diagnostics.
Acknowledgment
Disclosure statement: The authors declare no conflict of interest.
