Cannabidiol potentiates p53-driven autophagic cell death in non-small cell lung cancer following DNA damage: a novel synergistic approach beyond canonical pathways
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it