Autophagy inhibition enhances sensitivity of alpelisib in PI3K–mutated non-small cell lung cancer
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) is a prevalent and lethal form of lung cancer with few effective treatment options, and targeted therapies for PI3K-mutated NSCLC remain particularly limited. The phosphatidylinositol 3-kinase (PI3K) pathway, frequently activated in NSCLC, is a viable therapeutic target, especially in tumors harboring PI3K mutations. Alpelisib (BYL719), a selective PI3Kα inhibitor, has shown promise, but its efficacy is often hampered by compensatory survival mechanisms, including autophagy. This study assesses the therapeutic potential of alpelisib as a monotherapy and in combination with an autophagy inhibitor for PI3K-mutated NSCLC. Alpelisib significantly reduced cell viability in human NSCLC cell lines in a dose- and time-dependent manner, with enhanced markers of autophagy and apoptosis, with pronounced effects in PI3K-mutant H460 cells. Co-treatment with alpelisib and chloroquine (CQ) further suppressed tumor cell growth, viability, migration, and colony formation more effectively than alpelisib alone, owing to increased apoptosis, elevated early and late apoptotic populations, and increased levels of cleaved PARP and caspase-3. In xenograft mouse models, the combination of alpelisib and CQ significantly inhibited tumor growth and reduced EGFR-Ras signaling compared to monotherapy. These findings suggest that combining alpelisib with autophagy inhibition significantly enhances its antitumor activity in PI3K-mutated NSCLC, highlighting a promising therapeutic strategy to address unmet clinical needs in this molecular subset. This discovery opens new possibilities for developing innovative targeted therapies for challenging NSCLC.
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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) | 0.001 | 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".