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Record W4403689952 · doi:10.1038/s41418-024-01391-6

TP53: the unluckiest of genes?

2024· review· en· W4403689952 on OpenAlexafffund
Andreas C. Joerger, Thorsten Stiewe, Thierry Soussi

Bibliographic record

VenueCell Death and Differentiation · 2024
Typereview
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsStructural Genomics Consortium
FundersGenentechCancéropôle Ile de FranceBundesministerium für Bildung und ForschungInstitut National de la Santé et de la Recherche MédicaleOntario Genomics InstituteEuropean Federation of Pharmaceutical Industries and AssociationsWilhelm Sander-StiftungMerck KGaAOntario GenomicsGenome CanadaPfizerMcGill UniversityBayerDeutsche ForschungsgemeinschaftDeutsches Zentrum für LungenforschungBristol-Myers Squibb
KeywordsGeneBiologyGeneticsPhenotypeSuppressorTranscription factorMutationLoss functionCancerFunction (biology)GenomeCancer research

Abstract

fetched live from OpenAlex

The transcription factor p53 plays a key role in the cellular defense against cancer development. It is inactivated in virtually every tumor, and in every second tumor this inactivation is due to a mutation in the TP53 gene. In this perspective, we show that this diverse mutational spectrum is unique among all other cancer-associated proteins and discuss what drives the selection of TP53 mutations in cancer. We highlight that several factors conspire to make the p53 protein particularly vulnerable to inactivation by the mutations that constantly plague our genome. It appears that the TP53 gene has emerged as a victim of its own evolutionary past that shaped its structure and function towards a pluripotent tumor suppressor, but came with an increased structural fragility of its DNA-binding domain. TP53 loss of function - with associated dominant-negative effects - is the main mechanism that will impair TP53 tumor suppressive function, regardless of whether a neomorphic phenotype is associated with some of these variants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.297
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2024
Admission routes2
Has abstractyes

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