USP10/GSK3β-mediated inhibition of PTEN drives resistance to PI3K inhibitors in breast cancer
Bibliographic record
Abstract
Activating mutations in PIK3CA, the gene encoding the catalytic p110α subunit of PI3K, are some of the most frequent genomic alterations in breast cancer. Alpelisib, a small-molecule inhibitor that targets p110α, is a recommended drug for patients with PIK3CA-mutant advanced breast cancer. However, clinical success for PI3K inhibitors (PI3Kis) has been limited by their narrow therapeutic window. The lipid phosphatase PTEN is a potent tumor suppressor and a major negative regulator of the PI3K pathway. Unsurprisingly, inactivating mutations in PTEN correlate with tumor progression and resistance to PI3K inhibition due to persistent PI3K signaling. Here, we demonstrate that PI3K inhibition leads rapidly to the inactivation of PTEN. Using a functional genetic screen, we show that this effect is mediated by a USP10-GSK3β signaling axis, in which USP10 stabilizes GSK3β, resulting in GSK3β-mediated phosphorylation of the C-terminal tail of PTEN. This phosphorylation inhibits PTEN dimerization and thus prevents its activation. Downregulation of GSK3β or USP10 resensitizes PI3Ki-resistant breast cancer models and patient-derived organoids to PI3K inhibition and induces tumor regression. Our study establishes that enhancing PTEN activity is a new strategy to treat PIK3CA mutant tumors and provides a strong rationale for pursuing USP10 inhibitors in the clinic.
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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".