Cannabidiol potentiates p53-driven autophagic cell death in non-small cell lung cancer following DNA damage: a novel synergistic approach beyond canonical pathways
Bibliographic record
Abstract
The search for more effective and safer cancer therapies has led to an increasing interest in combination treatments that use well-established agents. Here we explore the potential of cannabidiol (CBD), a compound derived from cannabis, to enhance the anticancer effects of etoposide in non-small cell lung cancer (NSCLC). Although CBD is primarily used to manage childhood epilepsy, its broader therapeutic applications are being actively investigated, particularly in oncology. Our results revealed that, among various tested chemotherapeutic drugs, etoposide showed the most significant reduction in NSCLC cell viability when combined with CBD. To understand this synergistic effect, we conducted extensive transcriptomic and proteomic profiling, which showed that the combination of CBD and etoposide upregulated genes associated with autophagic cell death while downregulating key oncogenes known to drive tumor progression. This dual effect on cell death and oncogene suppression was mediated by inactivation of the PI3K-AKT-mTOR signaling pathway, a crucial regulator of cell growth and survival, and was found to be dependent on the p53 status. Interestingly, our analysis revealed that this combination therapy did not rely on traditional cannabinoid receptors or transient receptor potential cation channels, indicating that CBD exerts its anticancer effects through novel, noncanonical mechanisms. The findings suggest that the combination of CBD with etoposide could represent a groundbreaking approach to NSCLC treatment, particularly in cases where conventional therapies fail. By inducing autophagic cell death and inhibiting oncogenic pathways, this therapeutic strategy offers a promising new avenue for enhancing treatment efficacy in NSCLC, especially in tumors with p53 function.
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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".