The IARC TP53 Mutation Database compiles all TP53 gene variations identified in human populations and tumor samples. Data are compiled from the peer-reviewed literature and from generalist databases. The following datasets are available: # TP53 somatic mutations in sporadic cancers # TP53 germline mutation in familial cancers # Common TP53 polymorphisms identified in human populations # Functional and structural properties of P53 mutant proteins # TP53 gene status in human cell-lines # Mouse-models with engineered TP53 The database includes various annotations on the predicted or experimentally assessed functional impact of mutations, clinicopathologic characteristics of tumors and demographic and life-style information on patients. The database is meant to be a source of information on TP53 mutations for a broad range of scientists and clinicians who work in different research areas: # Basic research, to study the structural and functional aspects of the p53 protein # Molecular pathology of cancer, to understand the clinical significance of mutations identified in cancer patients # Molecular epidemiology of cancer, to analyze the links between specific exposures and mutation patterns and to make inferences about possible causes of cancer # Molecular genetics, to analyze genotype/phenotype relationships

Database compiles various types of data and information from literature and generalist databases on human TP53 gene variations related to cancer.

The database of germline p53 mutations is a comprehensive database covering all published cases of germline p53 mutations. The current version lists 580 tumors in 448 individuals belonging to 122 independent pedigrees. The database describes each p53 mutation (type of the mutation, exon and codon affected by the mutation, nucleotide and amino acid change), each family (family history of cancer, diagnosis of Li-Fraumeni syndrome), each affected individual (sex, generation, p53 status, from which parent the mutation was inherited) and each tumour (type, age of onset, p53 status-loss of heterozygosity, immunostaining). Each entry contains the original reference(s).

http://www.kegg.jp/ (mmu04115) p53 activation is induced by a number of stress signals, including DNA damage, oxidative stress and activated oncogenes. The p53 protein is employed as a transcriptional activator of p53-regulated genes. This results in three major outputs; cell cycle arrest, cellular senescence or apoptosis. Other p53-regulated gene functions communicate with adjacent cells, repair the damaged DNA or set up positive and negative feedback loops that enhance or attenuate the functions of the p53 protein and integrate these stress responses with other signal transduction pathways.

°, Nucleotide number; 1, The bolded bases indicate the base change; 2, Functional predictions derived from a computer model that takes into account the 3D structure of wild-type and mutant proteins and is trained on the trans activation dataset from Kato et al. Mutations are classified as “functional” or “non-functional”. More details here: http://www-p53.iarc.fr/Help.html#StructureClass; a, Frequencies reported in IARC database (http://www.iarc.fr/p53/) release October 2006. The frequencies are based on a total of 22822 reported mutations in all type of cancer and in 2274 reported mutations in breast cancer (brackets); T N M, TNM-classification, AJCC 2002 = UICC 2002, T, size or direct of the primary tumor; N, spread to regional lymph nodes; M, distant metastasis; ˆ, “F” followed by a number indicates that the patient was free of disease at that number of months of follow-up. “R” followed by a number indicates that the patient was alive at that number of months of follow-up but had suffered a relapse; ®, Site of relapse L, Locoregional; S, Skeletal; V; Visceral; *, “A” followed by a number indicates that the patient was alive at that number of months of follow-up. “D” followed by a number indicates that the patient died at that number of months of follow-up; ‡, Characterized as a mutation affecting L2/L3 domain, since it leads to truncation of the protein and will mostly affect L2/L3 domain; AI, Allelic imbalance; NA, Not available; ND, not done; NI, Not informative.

Protein-Protein, Genetic, and Chemical Interactions for Liu K (2011):TopBP1 mediates mutant p53 gain of function through NF-Y and p63/p73. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Nearly half of human cancers harbor p53 mutations, which can promote cancerous growth, metastasis, and resistance to therapy. The gain of function of mutant p53 is partly mediated by its ability to form a complex with NF-Y or p63/p73. Here, we demonstrate that TopBP1 mediates these activities in cancer, and we provide both in vitro and in vivo evidence to support its role. We show that TopBP1 interacts with p53 hot spot mutants and NF-YA and promotes mutant p53 and p300 recruitment to NF-Y target gene promoters. TopBP1 also facilitates mutant p53 interaction with and inhibition of the transcriptional activities of p63/p73. Depletion of TopBP1 in mutant p53 cancer cells leads to downregulation of NF-Y target genes cyclin A and Cdk1 and upregulation of p63/p73 target genes such as Bax and Noxa. Mutant p53-mediated resistance to chemotherapeutic agents depends on TopBP1. The growth-promoting activity of mutant p53 in a xenograft model also requires TopBP1. Thus, TopBP1 mediates mutant p53 gain of function in cancer. Since TopBP1 is often overexpressed in cancer cells and is recruited to cooperate with mutant p53 for tumor progression, TopBP1/mutant p53 interaction may be a new therapeutic target in cancer.

We mapped the genomic binding sites of the tumor suppressor protein p53 in the human colorectal cancer cell line HCT116 and report here that the binding patterns of endogenous wild type p53 differed significantly between the genomes of the cancer cell line HCT116 and the normal human IMR90 fibroblasts (GSE31558) under the same experimental conditions (6 hr treatment with 5-fluorouracil). p53 binding differences affect promoter regions, CpG islands and major families of human repeat elements such as LTR, LINE and SINE. While p53 genomic binding sites residing in repeats have been reported before, we show here that the fraction of the p53 genomic binding sites residing in different repeat families differs between the normal and cancer human cell lines. We confirm that the p53 genomic binding sites in HCT116 cells are excluded from CpG islands, an observation we made previously based on analysis of data reported by others. While the p53 ability to elicit stress-specific and cell-type-specific responses is well documented, how this specificity is established, at the level of binding to the genome and/or during post-binding events, represents an open question. Our data indicate that p53 binding to the human genome is cell line-specific and highly selective. The differences in the p53 genome-wide binding patterns between the cancer cell line HCT116 and the normal cell line IMR90, namely exclusion from CpG islands and enrichment at repeats in HCT116, likely reflect cancer-associated epigenetic changes in the chromatin. Overall design: Identification of genomic p53 binding sites in HCT116 cells by ChIP-seq.

Protein-Protein, Genetic, and Chemical Interactions for Yue X (2016):A novel mutant p53 binding partner BAG5 stabilizes mutant p53 and promotes mutant p53 GOFs in tumorigenesis. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Tumor suppressor p53 is the most frequently mutated gene in human tumors. Many tumor-associated mutant p53 (mutp53) proteins gain new tumor-promoting activities, including increased proliferation, metastasis and chemoresistance of tumor cells, which are defined as gain-of-functions (GOFs). Mutp53 proteins often accumulate at high levels in human tumors, which is important for mutp53 to exert their GOFs. The mechanism underlying mutp53 proteins accumulation in tumors is not fully understood. Here, we report that BAG5, a member of Bcl-2-associated athanogene (BAG) family proteins, promotes mutp53 accumulation in tumors, which in turn enhances mutp53 GOFs. Mechanistically, BAG5 interacts with mutp53 proteins to protect mutp53 from ubiquitination and degradation by E3 ubiquitin ligases MDM2 and CHIP, which in turn promotes mutp53 protein accumulation and therefore GOFs in promoting cell proliferation, tumor growth, cell migration and chemoresistance. BAG5 is frequently overexpressed in many human tumors and the overexpression of BAG5 is associated with poor prognosis of cancer patients. Altogether, this study revealed that inhibition of mutp53 degradation by BAG5 is a novel and critical mechanism underlying mutp53 protein accumulation and GOFs in cancer. Furthermore, our results also uncovered that promoting mutp53 accumulation and GOFs is a novel mechanism of BAG5 in tumorigenesis.

Protein-Protein, Genetic, and Chemical Interactions for Wang T (2001):hADA3 is required for p53 activity. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The tumor suppressor protein p53 is a transcription factor that is frequently mutated in human cancers. In response to DNA damage, p53 protein is stabilized and activated by post-translational modifications that enable it to induce either apoptosis or cell cycle arrest. Using a novel yeast p53 dissociator assay, we identify hADA3, a part of histone acetyltransferase complexes, as an important cofactor for p53 activity. p53 and hADA3 physically interact in human cells. This interaction is enhanced dramatically after DNA damage due to phosphorylation event(s) in the p53 N-terminus. Proper hADA3 function is essential for full transcriptional activity of p53 and p53-mediated apoptosis.

RNA was extracted utilizing the Direct-Zol RNA Microprep kit (Zymo Research, R2060). Barcoded Illumina-compatible stranded total RNA libraries were prepared using the TruSeq Stranded Total RNA kit (Illumina) as previously described. Library pools were quantified by qPCR and sequenced on the HiSeq 4000 sequencer using the 75-bp paired-end format. The raw RNA-seq readouts were subsequently mapped to the mouse mm10 assembly reference genome using TopHat2 and analyzed with DESeq2 (Bioconductor package).

Mutational inactivation of TP53 is a common event in cancer. Germline mutations in TP53 that inactivate this protein also occur in Li Fraumeni syndrome, which predisposes to early-onset cancer. In addition, there are dozens of other germline variants in TP53 that do not completely inactivate the function of this protein. In many cases studies have shown strong support for an impact of these lesser-functioning hypomorphs with increased cancer risk in humans and mouse models; however, the majority of these hypomorphs have yet to be categorized as pathogenic in clinical genetics databases. There is thus need for a functional assay to distinguish lesser-functioning hypomorphic p53 variants from wild type p53, or benign, fully-functional, variants. We report the surprising finding that two different African-centric genetic hypomorphs of p53, which occur in distinct functional domains of the protein, share common activities. We show that the Pro47Ser variant in the transactivation domain and the Tyr107His variant in the DNA binding domain both share increased propensity to misfold into a conformation specific for mutant p53. Moreover, cells and tissues with these variants show increased NF-B activity. We have identified a common gene signature from unstressed lymphocyte cell lines that is shared between these two, and other, genetic missense hypomorphs of TP53. We show that this gene signature successfully distinguishes wild type p53 and a benign p53 variant from lesser-functioning hypomorphic variants. These findings should allow us to better understand how hypomorphic variants contribute to cancer risk, and to better inform cancer risk in hypomorph carriers. RNA-seq of samples with wild type or hypomorphic p53

This Zenodo repository accompanies the manuscript "p53motifDB: integration of genomic information and tumor suppressor p53 binding motifs" and contains raw data tables, a Shiny app (via dockerfile), and a sqlite database that makes up the p53motifDB (p53 motif database).

The p53motifDB is a compendium of genomic locations in the human hg38 reference genome that contain recognizable DNA sequences that match the binding preferences for the transcription factor p53. Multiple types of genomic, epigenomic, and genome variation data were integrated with these locations in order to let researchers quickly generate hypotheses about novel activities of p53 or validate known behaviors.

Raw data tables

The raw data tables (raw_tables.tar.gz) are divided into the "primary" table, containing p53 motif locations and other biographical information relating to those genomic locations. The "accesory" tables contain additional descriptive or quantitative information that can be queried based on the information in the "primary" table. A description of table schema for the primary table and all accessory tables can be found in Schema_p53motifDB.xlsx.

Table_1_DataSources_V2.xlsx contains information about all raw and processed data sources that were used in the construction of the p53motifDB.

Shiny App

The Shiny App is designed to allow rapid filtering, querying, and downloading of the primary and accessory tables. Users can access a web-based version at https://p53motifDB.its.albany.edu. Users can also deploy the Shiny app locally by downloading and extracting p53motifDB_shiny_V2.zip and doing one of of the following:

Option 1: From the extracted folder, run the included Dockerfile to create a Docker image which will deploy to localhost port 3838.

Option 2: From the shiny_p53motifDB subfolder, run app_rev4.R from R or RStudio. This requires a number of dependencies, which may not be compatible with your current version of R. We highly recommend accessing the Shiny app via the web or through the Dockerfile.

sqlite Database

Users can perform more complex database queries (beyond those available in the Shiny app) by first downloading sqlite_db.tar.gz. Unpacking this file will reveal the database file p53motifDB.db. This is a sqlite database file containing the same "primary" and "accessory" data from raw_tables.tar.gz and can be used/queried using standard structured query language. The schema of this database including information about each table and the column contents can be examined in the file Schema_p53motifDB.xlsx.

The gzipped TAR file sqlite_db_R2.zip also contains all of the files and information neccessary to reconstruct the p53motifDB.db via R. Users can source the included R script (database_sqlite_commit.R) or can open, examine, and run via RStudio. We strongly advise unpacking the TAR file which will produce a folder called sqlite_db and then running the included R script from within that folder using either source or running line-by-line in RStudio. The result of this script will be p53motifDB.db and an RData object (sqlite_construction.RData) written to the sqlite_db folder.

If opening and running database_sqlite_commit.R via RStudio, please uncomment line 10 and comment out lines 13 and 14.

Please also be aware of the minimal package dependencies in R. The included version of p53motifDB.db was created using R (v. 3.4.0) and the following packages (and versions) available via CRAN:

RSQLite (v. 2.3.7), DBI (v. 1.2.3), tidyverse (2.0.0), and utils (v. 4.3.0) packages

Credit

The p53motifDB was created by Morgan Sammons, Gaby Baniulyte, and Sawyer Hicks.

Please let us know if you have any questions, comments, or would like additional datasets included in the next version of the p53motifDB by contacting masammons(at)albany.edu

This repository is an update to prior Zenodo record 13351805, with DOI https://doi.org/10.5281/zenodo.13351804.

The dataset contains chemicals that were predicted to activate p53 using the biomarker or using a high throughput assay for p53. This dataset is associated with the following publication: Corton, J., K. Witt, and C. Yauk. Identification of p53 Activators in a Human Microarray Compendium. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 32(9): 1748-1759, (2019).

Protein-Protein, Genetic, and Chemical Interactions for Hedstroem E (2009):Tumor-specific induction of apoptosis by a p53-reactivating compound. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The tumor suppressor function of p53 is disabled in the majority of tumors, either by a point mutation of the p53 gene, or via MDM2-dependent proteasomal degradation. We have screened a chemical library using a cell-based assay and identified a low molecular weight compound named MITA which induced wild-type p53-dependent cell death in a variety of different types of human tumor cells, such as lung, colon and breast carcinoma cells, as well as in osteosarcoma and fibrosarcoma-derived cells. MITA inhibited p53-MDM2 interaction in vitro and in cells, which in turn prevented MDM2-mediated ubiquitination of p53 and resulted in a prolonged half-life and accumulation of p53 in tumor cells. Notably, p53 induction by MITA resulted in upregulated expression of p53 target genes MDM2, Bax, Gadd45 and PUMA, on protein and mRNA level. Importantly, neither p53 nor these target genes were induced in normal human fibroblasts (HDFs), which correlated with the absence of growth suppression in fibroblasts after treatment with MITA. However, upon activation of oncogenes in fibroblasts an induction and activation of p53 was observed, suggesting that activation of p53 by MITA occurs predominantly in tumor cells.

We report here genome-wide analysis of the tumor suppressor p53 binding sites in normal human cells. 743 high-confidence ChIP-seq peaks representing putative genomic binding sites were identified in normal IMR90 fibroblasts using a reference chromatin sample. More than 40 % were located within 2 kb of a transcription start site (TSS), a distribution similar to that documented for individually studied functional p53 binding sites and to date not observed by previous genome-wide studies. Nearly half of the high-confidence binding sites in the IMR90 cells reside in CpG islands, in marked contrast to sites reported in cancer-derived cells. The distinct genomic features of the IMR90 binding sites do not reflect a distinct preference for specific sequences, since the de novo developed p53 motif based on our study is similar to those reported by genome-wide studies of cancer cells. More likely the different chromatin landscape in normal compared to cancer-derived cells influences p53 binding via modulating availability of the sites. We compared the IMR90 ChIP-seq peaks to the recently published IMR90 methylome1, and demonstrated that they are enriched at hypomethylated DNA. Our study represents the first genome-wide, de novo mapping of p53 binding sites in normal human cells and reveals that p53 binding sites reside in distinct genomic landscapes in normal and cancer-derived human cells. Identification of genomic p53 binding sites in normal human cells by ChIP-seq.

Protein-Protein, Genetic, and Chemical Interactions for Ding J (2022):EGFR suppresses p53 function by promoting p53 binding to DNA-PKcs: a noncanonical regulatory axis between EGFR and wild-type p53 in glioblastoma. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Epidermal growth factor receptor (EGFR) amplification and TP53 mutation are the two most common genetic alterations in glioblastoma multiforme (GBM). A comprehensive analysis of the TCGA GBM database revealed a subgroup with near mutual exclusivity of EGFR amplification and TP53 mutations indicative of a role of EGFR in regulating wild-type-p53 (wt-p53) function. The relationship between EGFR amplification and wt-p53 function remains undefined and this study describes the biological significance of this interaction in GBM.Mass spectrometry was used to identify EGFR-dependent p53-interacting proteins. The p53 and DNA-dependent protein kinase catalytic subunit (DNA-PKcs) interaction was detected by co-immunoprecipitation. We used CRISPR-Cas9 gene editing to knockout EGFR and DNA-PKcs and the Edit-R CRIPSR-Cas9 system for conditional knockout of EGFR. ROS activity was measured with a CM-H2DCFDA probe, and real-time PCR was used to quantify expression of p53 target genes.Using glioma sphere-forming cells (GSCs), we identified, DNA-PKcs as a p53 interacting protein that functionally inhibits p53 activity. We demonstrate that EGFR knockdown increased wt-p53 transcriptional activity, which was associated with decreased binding between p53 and DNA-PKcs. We further show that inhibition of DNA-PKcs either by siRNA or an inhibitor (nedisertib) increased wt-p53 transcriptional activity, which was not enhanced further by EGFR knockdown, indicating that EGFR suppressed wt-p53 activity through DNA-PKcs binding with p53. Finally, using conditional EGFR-knockout GSCs, we show that depleting EGFR increased animal survival in mice transplanted with wt-p53 GSCs.This study demonstrates that EGFR signaling inhibits wt-p53 function in GBM by promoting an interaction between p53 and DNA-PKcs.

Alteration of metabolism in cancer cells is a central aspect of the mechanisms that sustain aggressive traits. Aldo-keto reductase 1 B1 (AKR1B1) catalyzes the reduction of several aldehydes to alcohols consuming NADPH. Nevertheless, the ability of AKR1B1 to reduce different substrates renders difficult to comprehensively ascertain its biological role. Recent evidence has implicated AKR1B1 in cancer, however, the mechanisms underlying its pro-oncogenic function remain largely unknown. In this work, we report that AKR1B1 expression is controlled by the p53 tumor suppressor. We found that breast cancer patients bearing wild type TP53 have reduced AKR1B1 expression. In cancer cell lines, p53 reduced AKR1B1 mRNA and protein levels, and repressed promoter activity in luciferase assays. Furthermore, chromatin immunoprecipitation assays indicated that p53 is recruited to the AKR1B1 promoter. We also observed that AKR1B1 overexpression promoted metastasis in the 4T1 orthotopic model of triple negative breast cancer. Proteomic analysis of 4T1 cells overexpressing AKR1B1 showed that AKR1B1 exerts a marked effect on proteins related to metabolism, with particular impact on mitochondrial function. This work provides novel insights on the link between the p53 pathway and metabolism in cancer cells and contributes to characterizing the alterations associated to the pathologic role of AKR1B1.

Molecular dynamics simulation of the VUS - 1 based on the TP53 database, the simulation is up to 100 ns.

Protein-Protein, Genetic, and Chemical Interactions for Chander H (2010):Skp2B attenuates p53 function by inhibiting prohibitin. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The F-box protein Skp2 and its isoform Skp2B are both overexpressed in breast cancers. Skp2 alters the activity of p53 by inhibiting its interaction with p300 and by promoting p300 degradation. Here, we report that Skp2B also attenuates the activity of p53; however, this effect is independent of p300, suggesting that another mechanism might be involved. Prohibitin, a protein reported to activate p53, was isolated in a two-hybrid screen with the carboxy-terminal domain unique to Skp2B. We observed that prohibitin is a new substrate of Skp2B and that the degradation of prohibitin is responsible for the attenuated activity of p53 in cells overexpressing Skp2B. Furthermore, we show that the activity of p53 is reduced in the mammary glands of Skp2B transgenic mice. This study indicates that both Skp2 and Skp2B attenuate p53 activity through different pathways, suggesting that amplification of the Skp2 locus represents a powerful mechanism to attenuate p53 function in cancer.

Raw imaging data that was used to generate the supplementary figures S3 - S7 for Akama-Garren et al. 2023 "Regulation of immunological tolerance by the p53-inhibitor iASPP". Please refer to the paper in Cell Death & Disease for more explanation. All ChIP-seq and mNET-seq data generated in this study have been deposited in the GEO database and are available under primary accession number GSE202445. RNA-seq data generated in this study have been deposited in the GEO database and are available under primary accession number GSE218389. Any additional information required to reanalyse the data reported in this paper is available from the lead contacts upon request.