A rare DNA contact mutation in cancer confers p53 gain‐of‐function and tumor cell survival via TNFAIP8 induction
Bibliographic record
Abstract
The p53 tumor suppressor gene encodes a sequence-specific transcription factor. Mutations in the coding sequence of p53 occur frequently in human cancer and often result in single amino acid substitutions (missense mutations) in the DNA binding domain (DBD), blocking normal tumor suppressive functions. In addition to the loss of canonical functions, some missense mutations in p53 confer gain-of-function (GOF) activities to tumor cells. While many missense mutations in p53 cluster at six "hotspot" amino acids, the majority of mutations in human cancer occur elsewhere in the DBD and at a much lower frequency. We report here that mutations at K120, a non-hotspot DNA contact residue, confer p53 with the previously unrecognized ability to bind and activate the transcription of the pro-survival TNFAIP8 gene. Mutant K120 p53 binds the TNFAIP8 locus at a cryptic p53 response element that is not occupied by wild-type p53. Furthermore, induction of TNFAIP8 is critical for the evasion of apoptosis by tumor cells expressing the K120R variant of p53. These findings identify induction of pro-survival targets as a mechanism of gain-of-function activity for mutant p53 and will likely broaden our understanding of this phenomenon beyond the limited number of GOF activities currently reported for hotspot mutants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".