TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.

Correctly identifying the true driver mutations in a patient's tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53, integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model's predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes.

Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): a cloud-based platform for curating and classifying germline variants.

Variant interpretation in the era of massively parallel sequencing is challenging. Although many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here, we present the Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE), a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in variant call format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4000 pediatric cancer patients and serves as a repository for the expert-reviewed results. PeCanPIE was originally developed for pediatric cancer but can be easily extended for use for nonpediatric cancers and noncancer genetic diseases. Although PeCanPIE's web-based interface was designed to be accessible to non-bioinformaticians, its back-end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

High prevalence of cancer-associated TP53 variants in the gnomAD database: A word of caution concerning the use of variant filtering.

The 1,000 genome project, the Exome Aggregation Consortium (ExAC) or the Genome Aggregation database (gnomAD) datasets, were developed to provide large-scale reference data of genetic variations for various populations to filter out common benign variants and identify rare variants of clinical importance based on their frequency in the human population. Using a TP53 repository of 80,000 cancer variants, as well as TP53 variants from multiple cancer genome projects, we have defined a set of certified oncogenic TP53 variants. This specific set has been independently validated by functional and in silico predictive analysis. Here we show that a significant number of these variants are included in gnomAD and ExAC. Most of them correspond to TP53 hotspot variants occurring as somatic and germline events in human cancer. Similarly, disease-associated variants for five other tumor suppressor genes, including BRCA1, BRCA2, APC, PTEN, and MLH1, have also been identified. This study demonstrates that germline TP53 variants in the human population are more frequent than previously thought. Furthermore, population databases such as gnomAD or ExAC must be used with caution and need to be annotated for the presence of oncogenic variants to improve their clinical utility.

Seshat: A Web service for accurate annotation, validation, and analysis of TP53 variants generated by conventional and next-generation sequencing.

Accurate annotation of genomic variants in human diseases is essential to allow personalized medicine. Assessment of somatic and germline TP53 alterations has now reached the clinic and is required in several circumstances such as the identification of the most effective cancer therapy for patients with chronic lymphocytic leukemia (CLL). Here, we present Seshat, a Web service for annotating TP53 information derived from sequencing data. A flexible framework allows the use of standard file formats such as Mutation Annotation Format (MAF) or Variant Call Format (VCF), as well as common TXT files. Seshat performs accurate variant annotations using the Human Genome Variation Society (HGVS) nomenclature and the stable TP53 genomic reference provided by the Locus Reference Genomic (LRG). In addition, using the 2017 release of the UMD_TP53 database, Seshat provides multiple statistical information for each TP53 variant including database frequency, functional activity, or pathogenicity. The information is delivered in standardized output tables that minimize errors and facilitate comparison of mutational data across studies. Seshat is a beneficial tool to interpret the ever-growing TP53 sequencing data generated by multiple sequencing platforms and it is freely available via the TP53 Website, http://p53.fr or directly at http://vps338341.ovh.net/.

Synonymous Somatic Variants in Human Cancer Are Not Infamous: A Plea for Full Disclosure in Databases and Publications.

Single-nucleotide variants (SNVs) are the most frequent genetic changes found in human cancer. Most driver alterations are missense and nonsense variants localized in the coding region of cancer genes. Unbiased cancer genome sequencing shows that synonymous SNVs (sSNVs) can be found clustered in the coding regions of several cancer oncogenes or tumor suppressor genes suggesting purifying selection. sSNVs are currently underestimated, as they are usually discarded during analysis. Furthermore, several public databases do not display sSNVs, which can lead to analytical bias and the false assumption that this mutational event is uncommon. Recent progress in our understanding of the deleterious consequences of these sSNVs for RNA stability and protein translation shows that they can act as strong drivers of cancer, as demonstrated for several cancer genes such as TP53 or BCL2L12. It is therefore essential that sSNVs be properly reported and analyzed in order to provide an accurate picture of the genetic landscape of the cancer genome.

TP53 mutations are early events in chronic lymphocytic leukemia disease progression and precede evolution to complex karyotypes.

TP53 abnormalities lead to resistance to purine analogues and are found in over 40% of patients with refractory chronic lymphocytic leukemia (CLL). At diagnosis, no more than 5% of patients carry the 17p deletion, most cases harbour mutations within the other TP53 allele. The incidence of a TP53 mutation as the only alteration is approximately 5%, but this depends on the sensitivity of the technique. Recently, having a complex karyotype has been considered a strong adverse prognostic factor. However, there are no longitudinal studies simultaneously examining the presence of the 17p deletion, TP53 mutations and karyotype abnormalities. We conducted a retrospective longitudinal study of 31 relapsed/refractory CLL patients. Two to six blood samples per patient were analyzed, with a median follow-up of 8 years. In this report, we assessed the sequence of events of TP53 clonal evolution and correlated the presence of TP53 abnormalities to genetic instability during progression and treatment. Next-generation sequencing allowed the early detection of TP53 mutated clones and was able to be performed on a routine basis, demonstrating an excellent correlation between the Illumina and Ion Torrent technologies. We concluded that TP53 mutations are early events and precede clonal evolution to complex karyotypes. We strongly recommend the early and iterated detection of TP53 mutations in progressive cases.

Shaping genetic alterations in human cancer: the p53 mutation paradigm.

p53 mutations are found in 50% of human cancers. Molecular epidemiology has shown strong correlations between the spectrum of p53 mutations and exposure to exogenous carcinogens. This spectrum is influenced quantitatively and qualitatively by various upstream genetic filters that modulate carcinogen activation, detoxification, and/or DNA repair. In this review, we will discuss how other factors such as tissue specificity, SNP of genes associated with the p53 pathway, other genetic alterations, or p53 mutant heterogeneity can act as a second set of downstream filters that also have a profound impact on the spectrum of p53 mutations.

Locus-specific mutation databases: pitfalls and good practice based on the p53 experience.

Between 50,000 and 60,000 mutations have been described in various genes that are associated with a wide variety of diseases. Reporting, storing and analysing these data is an important challenge as such data provide invaluable information for both clinical medicine and basic science. Locus-specific databases have been developed to exploit this huge volume of data. The p53 mutation database is a paradigm, as it constitutes the largest collection of somatic mutations (22,000). However, there are several biases in this database that can lead to serious erroneous interpretations. We describe several rules for mutation database management that could benefit the entire scientific community.

The TP53 website: an integrative resource centre for the TP53 mutation database and TP53 mutant analysis.

A novel resource centre for TP53 mutations and mutants has been developed (http://p53.fr). TP53 gene dysfunction can be found in the majority of human cancer types. The potential use of TP53 mutation as a biomarker for clinical studies or exposome analysis has led to the publication of thousands of reports describing the TP53 gene status in >10,000 tumours. The UMD TP53 mutation database was created in 1990 and has been regularly updated. The 2012 release of the database has been carefully curated, and all suspicious reports have been eliminated. It is available either as a flat file that can be easily manipulated or as novel multi-platform analytical software that has been designed to analyse various aspects of TP53 mutations. Several tools to ascertain TP53 mutations are also available for download. We have developed TP53MULTLoad, a manually curated database providing comprehensive details on the properties of 2549 missense TP53 mutants. More than 100,000 entries have been arranged in 39 different activity fields, such as change of transactivation on various promoters, apoptosis or growth arrest. For several hot spot mutants, multiple gain of function activities are also included. The database can be easily browsed via a graphical user interface.

Data-driven unbiased curation of the TP53 tumor suppressor gene mutation database and validation by ultradeep sequencing of human tumors.

Cancer mutation databases are expected to play central roles in personalized medicine by providing targets for drug development and biomarkers to tailor treatments to each patient. The accuracy of reported mutations is a critical issue that is commonly overlooked, which leads to mutation databases that include a sizable number of spurious mutations, either sequencing errors or passenger mutations. Here we report an analysis of the latest version of the TP53 mutation database, including 34,453 mutations. By using several data-driven methods on multiple independent quality criteria, we obtained a quality score for each report contributing to the database. This score can now be used to filter for high-confidence mutations and reports within the database. Sequencing the entire TP53 gene from various types of cancer using next-generation sequencing with ultradeep coverage validated our approach for curation. In summary, 9.7% of all collected studies, mostly comprising numerous tumors with multiple infrequent TP53 mutations, should be excluded when analyzing TP53 mutations. Thus, by combining statistical and experimental analyses, we provide a curated mutation database for TP53 mutations and a framework for mutation database analysis.

The TP53 gene network in a postgenomic era.

Inactivation of TP53 pathways are the most common defects observed in human cancer. Although missense mutations remain the most frequent genetic event, it is now evident that dysfunction of several members of this network such as MDM2, MDM4 (mdmX), or miR-125b can substitute for TP53 mutations. This special issue on TP53 brings the TP53 gene into the post-genomic era. Several fundamental features of wild type and mutant proteins and their modifications are reviewed, as well as animal models and clinical aspects such as recommendations for patient care. The complex structure of this gene warrants innovative strategies to infer a more accurate status of human tumors. Recommendations and guidelines for reporting and annotating TP53 variants are also provided, to help researchers generate standardized data that are easy to understand, analyze, and exchange across various cancer variant databases.

Locus-specific databases in cancer: what future in a post-genomic era? The TP53 LSDB paradigm.

Locus-specific databases (LSDBs) are curated compilations of sequence variants in genes associated with disease and have been invaluable tools for both basic and clinical research. These databases contain extensive information provided by the literature and benefit from manual curation by experts. Cancer genome sequencing projects have generated an explosion of data that are stored directly in centralized databases, thus possibly alleviating the need to develop independent LSDBs. A single cancer genome contains several thousand somatic mutations. However, only a handful of these mutations are truly oncogenic and identifying them remains a challenge. However, we can expect that this increase in data and the development of novel biocuration algorithms will ultimately result in more accurate curation and the release of stable sets of data. Using the evolution and content of the TP53 LSDB as a paradigm, it is possible to draw a model of gene mutation analysis covering initial descriptions, the accumulation and organization of knowledge in databases, and the use of this knowledge in clinical practice. It is also possible to make several assumptions on the future of LSDBs and how centralized databases could change the accessibility of data, with interfaces optimized for different types of users and adapted to the specificity of each region of the genome, coding or noncoding, associated with tumor development.

TP53 mutations in human cancer: database reassessment and prospects for the next decade.

More than 50% of human tumors carry TP53 gene mutations and in consequence more than 45,000 somatic and germline mutations have been gathered in the UMD TP53 database (http://p53.fr). Analyses of these mutations have been invaluable for bettering our knowledge on the structure-function relationships within the TP53 protein and the high degree of heterogeneity of the various TP53 mutants in human cancer. In this review, we discuss how with the release of the sequences of thousands of tumor genomes issued from high-throughput sequencing, the description of novel TP53 mutants is now reaching a plateau indicating that we are close to the full set of mutants that target the elusive tumor-suppressive activity of this protein. We performed an extensive and thorough analysis of the TP53 mutation database, focusing particularly on specific sets of mutations that were overlooked in the past because of their low frequencies, for example, synonymous mutations, splice mutations, or mutations-targeting residues subject to posttranslational modifications. We also discuss the evolution of the statistical methods used to differentiate TP53 passenger mutations and artifactual data from true mutations, a process vital to the release of an accurate TP53 mutation database that will in turn be an invaluable tool for both clinicians and researchers.

Recommendations for analyzing and reporting TP53 gene variants in the high-throughput sequencing era.

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").

Analysis of TP53 mutation status in human cancer cell lines: a reassessment.

Tumor-derived cell lines play an important role in the investigation of tumor biology and genetics. Across a wide array of studies, they have been tools of choice for the discovery of important genes involved in cancer and for the analysis of the cellular pathways that are impaired by diverse oncogenic events. They are also invaluable for screening novel anticancer drugs. The TP53 protein is a major component of multiple pathways that regulate cellular response to various types of stress. Therefore, TP53 status affects the phenotype of tumor cell lines profoundly and must be carefully ascertained for any experimental project. In the present review, we use the 2014 release of the UMD TP53 database to show that TP53 status is still controversial for numerous cell lines, including some widely used lines from the NCI-60 panel. Our analysis clearly confirms that, despite numerous warnings, the misidentification of cell lines is still present as a silent and neglected issue, and that extreme care must be taken when determining the status of p53, because errors may lead to disastrous experimental interpretations. A novel compendium gathering the TP53 status of 2,500 cell lines has been made available (http://p53.fr). A stand-alone application can be used to browse the database and extract pertinent information on cell lines and associated TP53 mutations. It will be updated regularly to minimize any scientific issues associated with the use of misidentified cell lines (http://p53.fr).

ERIC recommendations for TP53 mutation analysis in chronic lymphocytic leukemia-2024 update.

In chronic lymphocytic leukemia (CLL), analysis of TP53 aberrations (deletion and/or mutation) is a crucial part of treatment decision-making algorithms. Technological and treatment advances have resulted in the need for an update of the last recommendations for TP53 analysis in CLL, published by ERIC, the European Research Initiative on CLL, in 2018. Based on the current knowledge of the relevance of low-burden TP53-mutated clones, a specific variant allele frequency (VAF) cut-off for reporting TP53 mutations is no longer recommended, but instead, the need for thorough method validation by the reporting laboratory is emphasized. The result of TP53 analyses should always be interpreted within the context of available laboratory and clinical information, treatment indication, and therapeutic options. Methodological aspects of introducing next-generation sequencing (NGS) in routine practice are discussed with a focus on reliable detection of low-burden clones. Furthermore, potential interpretation challenges are presented, and a simplified algorithm for the classification of TP53 variants in CLL is provided, representing a consensus based on previously published guidelines. Finally, the reporting requirements are highlighted, including a template for clinical reports of TP53 aberrations. These recommendations are intended to assist diagnosticians in the correct assessment of TP53 mutation status, but also physicians in the appropriate understanding of the lab reports, thus decreasing the risk of misinterpretation and incorrect management of patients in routine practice whilst also leading to improved stratification of patients with CLL in clinical trials.