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TP53 aberrations are a major predictive factor of resistance to chemoimmunotherapy in chronic lymphocytic leukemia (CLL), and an assessment of them before each line of treatment is required for theranostic stratification. Acquisition of subclonal TP53 abnormalities underlies the evolution of CLL. To better characterize the distribution, combination, and impact of TP53 variants in CLL, 1,056 TP53 variants collected from 683 patients included in a multicenter collaborative study in France were analyzed and compared to UMD_CLL, a dataset built from published articles collectively providing 5,173 TP53 variants detected in 3,808 patients. Our analysis confirmed the presence of several CLL-specific hotspot mutations, including a two-base pair deletion in codon 209 and a missense variant at codon 234, the latter being associated with alkylating treatment. Our analysis also identified a novel CLL-specific variant in the splice acceptor signal of intron 6 leading to the use of a cryptic splice site, similarly utilized by TP53 to generate p53psi, a naturally truncated p53 isoform localized in the mitochondria. Examination of both UMD_CLL and several recently released large-scale genomic analyses of CLL patients confirmed that this splice variant is highly enriched in this disease when compared to other cancer types. Using a TP53-specific single-nucleotide polymorphism, we also confirmed that copy-neutral loss of heterozygosity is frequent in CLL. This event can lead to misinterpretation of TP53 status. Unlike other cancers, CLL displayed a high proportion of patients harboring multiple TP53 variants. Using both in silico analysis and single molecule smart sequencing, we demonstrated the coexistence of distinct subclones harboring mutations on distinct alleles. In summary, our study provides a detailed TP53 mutational architecture in CLL and gives insights into how treatments may shape the genetic landscape of CLL patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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/. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 (). 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 (). [ABSTRACT FROM AUTHOR]

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'). [ABSTRACT FROM AUTHOR]

ABSTRACT 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 (). 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. [ABSTRACT FROM AUTHOR]