Pharmacological targeting of p53 through RITA is an effective antitumoral strategy for malignant pleural mesothelioma
Bibliographic record
Abstract
Malignant mesothelioma, a very aggressive tumor associated to asbestos exposure, is expected to increase in incidence, and unfortunately, no curative modality exists. Reactivation of p53 is a new attractive antitumoral strategy. p53 is rarely mutated in mesothelioma, but it is inactivated in most tumors by the lack of p14(ARF). Here, we evaluated the feasibility of this approach in pleural mesothelioma by testing RITA and nutlin-3, two molecules able to restore p53 function through a different mechanism, on a panel of mesothelioma cell lines representing the epithelioid (NCI-H28, NCI-H2452, IST-MES 2), biphasic (MSTO-211H), and sarcomatoid (NCI-H2052) histotypes compared with the normal mesothelial HMC-hTERT. RITA triggered robust caspase-dependent apoptosis specifically in epithelioid and biphasic mesothelioma cell lines, both through wild-type and mutant p53, concomitant to p21 downregulation. Conversely, nutlin-3 induced a p21-dependent growth arrest, rather than apoptosis, and was slightly toxic on HMC-hTERT. Interestingly, we identified a previously undetected point mutation of p53 (p.Arg249Ser) in IST-MES 2, and showed that RITA is also able to reactivate this p53 mutant protein and its apoptotic function. RITA reduced tumor growth in a MSTO-211H-derived xenograft model of mesothelioma and synergized with cisplatin, which is the mainstay of treatment for this tumor. Our data indicate that reactivation of p53 and concomitant p21 downregulation effectively induce cell death in mesothelioma, a tumor characterized by a high intrinsic resistance to apoptosis. Altogether, our findings provide the preclinical framework supporting the use of p53-reactivating agents alone, or in combination regimens, to improve the outcome of patients with mesothelioma.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".