p53 mutants cooperate with HIF-1 in transcriptional regulation of extracellular matrix components to promote tumor progression
Bibliographic record
Abstract
gene and microenvironmentally driven activation of hypoxia-inducible factor-1 (HIF-1) typically occur in later stages of tumorigenesis. An ongoing challenge is the identification of molecular determinants of advanced cancer pathogenesis to design alternative last-line therapeutic options. Here, we report that p53 mutants influence the tumor microenvironment by cooperating with HIF-1 to promote cancer progression. We demonstrate that in non-small cell lung cancer (NSCLC), p53 mutants exert a gain-of-function (GOF) effect on HIF-1, thus regulating a selective gene expression signature involved in protumorigenic functions. Hypoxia-mediated activation of HIF-1 leads to the formation of a p53 mutant/HIF-1 complex that physically binds the SWI/SNF chromatin remodeling complex, promoting expression of a selective subset of hypoxia-responsive genes. Depletion of p53 mutants impairs the HIF-mediated up-regulation of extracellular matrix (ECM) components, including type VIIa1 collagen and laminin-γ2, thus affecting tumorigenic potential of NSCLC cells in vitro and in mouse models in vivo. Analysis of surgically resected human NSCLC revealed that expression of this ECM gene signature was highly correlated with hypoxic tumors exclusively in patients carrying p53 mutations and was associated with poor prognosis. Our data reveal a GOF effect of p53 mutants in hypoxic tumors and suggest synergistic activities of p53 and HIF-1. These findings have important implications for cancer progression and might provide innovative last-line treatment options for advanced NSCLC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".