PI3K Inhibitors Curtail MYC-Dependent Mutant p53 Gain-of-Function in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: mutation frequently leads to the synthesis of mutant p53 proteins with gain-of-function activity, associated with radioresistance and high incidence of local recurrences in HNSCC. EXPERIMENTAL DESIGN: Mutant p53-associated functions were investigated through gene set enrichment analysis in the Cancer Genome Atlas cohort of HNSCC and in a panel of 22 HNSCC cell lines. Mutant p53-dependent transcripts were analyzed in HNSCC cell line Cal27, carrying mutant p53H193L; FaDu, carrying p53R248L; and Detroit 562, carrying p53R175H. Drugs impinging on mutant p53-MYC-dependent signature were identified interrogating Connectivity Map (https://clue.io) derived from the Library of Integrated Network-based Cellular Signatures (LINCS) database (http://lincs.hms.harvard.edu/) and analyzed in HNSCC cell lines and patient-derived xenografts (PDX) models. RESULTS: We identified a signature of transcripts directly controlled by gain-of-function mutant p53 protein and prognostic in HNSCC, which is highly enriched of MYC targets. Specifically, both in PDX and cell lines of HNSCC treated with the PI3Kα-selective inhibitor BYL719 (alpelisib) the downregulation of mutant p53/MYC-dependent signature correlates with response to this compound. Mechanistically, mutant p53 favors the binding of MYC to its target promoters and enhances MYC protein stability. Treatment with BYL719 disrupts the interaction of MYC, mutant p53, and YAP proteins with MYC target promoters. Of note, depletion of MYC, mutant p53, or YAP potentiates the effectiveness of BYL719 treatment. CONCLUSIONS: Collectively, the blocking of this transcriptional network is an important determinant for the response to BYL719 in HNSCC.
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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) | 0.002 | 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".