MicroRNA-mediated PTEN downregulation as a novel non-genetic mechanism of acquired resistance to PI3Kα inhibitors of head & neck squamous cell carcinoma
Bibliographic record
Abstract
AIMS: Head and neck squamous cell carcinomas (HNSCCs) frequently harbor alterations in the PI3K signalling axis and, particularly, in the PIK3CA gene. The promising rationale of using PI3K inhibitors for the treatment of HNSCC has, however, clashed with the spontaneous development of resistance over time. METHODS: To identify valuable targets for overcoming acquired resistance to PI3Kα inhibitors in HNSCC, we performed microRNA profiling on a cohort of HNSCC PDXs that were treated with alpelisib, including both responsive and resistant tumors. Using CRISPR/Cas9, siRNA, and PTEN-/- isogenic and alpelisib-resistant cell models, we examined the role of PTEN in resistance acquisition. Phospho-proteomic analysis identified PTEN-dependent phosphorylation events, while PI3Kα inhibitor-resistant organoids were used to assess PLK1 inhibitor efficacy. RESULTS: We identified microRNAs altered in resistant PDXs, including members of the miR-17-92 cluster. Mechanistically, we observed that the hyperactive c-Myc was recruited to MIR17HG regulatory regions in alpelisib-resistant cells, sustaining miR-17-5p, miR-19b-3p, and miR-20a-5p expression, which downregulated PTEN. PTEN knockout or depletion conferred alpelisib resistance in HNSCC cells. We identified PTEN-dependent phosphorylation events, such as p-PLK1-T210, involved in resistance. Interestingly, pharmacological inhibition of PLK1 strongly reduced the viability of PI3Kα-resistant organoids derived from HNSCC PDXs and cell line models. CONCLUSION: Overall, this study unveils a novel, microRNA-driven, non-genetic mechanism contributing to acquired resistance to PI3Kα inhibitors in HNSCC. Indeed, linking hyperactive c-Myc to sustain miR-17-92 expression and consequent PTEN downregulation, we also propose that targeting PTEN-dependent downstream effectors, such as PLK1, may offer a powerful therapeutic strategy for resistant 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".